第 1 卷（人物主线）：第 1–3 集 + 人物表
主角线：Guspu Frillyknots / Ral Fastenhatchets / Alath Blottedmine 由线索挖掘器自动选定，按因果链切集，经 5 维度自评闸门（≥12 分）后入库。
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Chen Yi
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"""每卷人物表：Markdown 表格 + 每人一两句小传。
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访谈决策：
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- 每卷开头一份人物表，表格 + 一两句小传，随年表一起入库
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- 人物驱动叙事里，读者记不住 3–6 人加对手配角的英文专名，所以必须有一份可回查的名册
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表格部分完全由史料推导（种族、生卒、出场次数、身份）；
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"身份"不是猜的，而是从事件字段反推——例如某人 8 次出现在 corruptor_hfid 上，
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就写"构陷者×8"。小传才交给模型写，且必须只使用给定事实。
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"""
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from __future__ import annotations
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import json
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import re
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from dataclasses import dataclass
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from pathlib import Path
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from dfannals import factcheck
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from dfannals.config import MAX_TOKENS_STRUCTURED, Gateway
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from dfannals.episodes import Episode
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from dfannals.legends import Event, World
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from dfannals.llm import chat
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from dfannals.threads import Thread
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# 事件字段 → 中文角色名（用于反推"身份"）
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ROLE_LABELS: dict[str, str] = {
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"corruptor_hfid": "构陷发起者",
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"target_hfid": "被针对者",
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"wounder_hfid": "行凶者",
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"woundee_hfid": "受伤者",
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"snatcher_hfid": "绑走者",
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"seeker_hfid": "求关系者",
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"winner_hfid": "胜者",
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"competitor_hfid": "参赛者",
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"slayer_hfid": "凶手",
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"hfid": "当事者",
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"hfid_target": "关系对象",
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"teacher_hfid": "师长",
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"student_hfid": "学徒",
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"persecutor_hfid": "迫害者",
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"expelled_hfid": "被逐者",
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"convicted_hfid": "被定罪者",
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"interrogator_hfid": "审讯者",
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"framer_hfid": "构陷者",
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"fooled_hfid": "受骗者",
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}
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BIO_LIMIT = 10 # 小传最多覆盖多少人
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EDGE_RE = re.compile(r"^```(?:json)?|```$", re.MULTILINE)
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@dataclass
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class CastRow:
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fid: int
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name: str
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race: str
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span: str
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role: str # 主角 / 对手 / 配角
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appearances: int
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identity: str
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def as_row(self) -> str:
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return (f"| {self.name} | {self.race or '—'} | {self.span} | {self.identity or '—'} | "
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f"{self.appearances} | {self.role} |")
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def _identity_of(world: World, fid: int, events: list[Event]) -> str:
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"""从事件字段反推这个人在这段历史里扮演什么。"""
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counts: dict[str, int] = {}
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for e in events:
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for tag, vals in e.fields.items():
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if tag not in ROLE_LABELS:
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continue
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for raw in vals:
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try:
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if int(raw) == fid:
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counts[tag] = counts.get(tag, 0) + 1
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except ValueError:
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continue
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if not counts:
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return ""
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ranked = sorted(counts.items(), key=lambda kv: -kv[1])[:2]
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return "、".join(f"{ROLE_LABELS[tag]}×{n}" for tag, n in ranked)
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def _span_of(thread: Thread, episodes: list[Episode]) -> str:
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"""表头跨度以实际切出的剧集为准（线索自身跨度只算成员之间的互动）。"""
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if not episodes:
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return thread.label
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return f"{episodes[0].start_year}–{episodes[-1].end_year}"
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def collect_rows(world: World, thread: Thread, episodes: list[Episode], max_others: int = 6) -> list[CastRow]:
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"""主角优先，其次是出场最多的对手/配角。"""
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events = [e for ep in episodes for e in ep.events] or thread.events
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appearances: dict[int, int] = {}
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for e in events:
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for fid in e.figure_ids():
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appearances[fid] = appearances.get(fid, 0) + 1
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members = set(thread.members)
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rows: list[CastRow] = []
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def make(fid: int, role: str) -> CastRow:
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fig = world.figures.get(fid)
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return CastRow(
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fid=fid,
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name=world.figure_name(fid),
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race=fig.race if fig else "",
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span=fig.alive_span if fig else "生卒不详",
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role=role,
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appearances=appearances.get(fid, 0),
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identity=_identity_of(world, fid, events),
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)
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for fid in sorted(members, key=lambda f: -appearances.get(f, 0)):
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rows.append(make(fid, "主角"))
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others = sorted((f for f in appearances if f not in members), key=lambda f: -appearances[f])
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for fid in others[:max_others]:
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rows.append(make(fid, "对手/配角"))
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return rows
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BIO_INSTRUCTIONS = """你在给一份矮人要塞编年史的人物表写小传。
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规则：
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- 只使用下面给出的事实。**不许编造任何新的人名、地名、组织名。**
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- 每人 1–2 句中文，口吻像编年史官写的旁注：可以刻薄、可以偏心，但事实必须来自材料。
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- 姓名一律保留英文原文。
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- 只输出 JSON，不要解释、不要代码块：{"英文名": "小传", ...}
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"""
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def _bio_prompt(world: World, rows: list[CastRow], events: list[Event]) -> str:
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lines = [BIO_INSTRUCTIONS, "", "材料："]
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for row in rows[:BIO_LIMIT]:
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facts = [
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f"{row.name}（{row.race or '种族不详'}，{row.span}，本卷出场 {row.appearances} 次，"
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f"身份：{row.identity or '不详'}，在故事里是{row.role}）"
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]
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for e in events:
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if row.fid not in e.figure_ids():
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continue
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refs = " · ".join(world.render_refs(e))
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facts.append(f" {e.year}年 {e.type} {refs}")
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if len(facts) > 5:
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break
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lines.extend(facts)
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return "\n".join(lines)
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def _parse_bios(raw: str) -> dict[str, str]:
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text = EDGE_RE.sub("", raw).strip()
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start, end = text.find("{"), text.rfind("}")
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if start < 0 or end <= start:
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raise ValueError(f"小传没有返回 JSON：{raw[:200]}")
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try:
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data = json.loads(text[start:end + 1])
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except json.JSONDecodeError as exc:
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raise ValueError(f"小传 JSON 无法解析：{exc}｜原文：{raw[:200]}") from None
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if not isinstance(data, dict):
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raise ValueError("小传返回的不是对象")
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return {str(k): str(v) for k, v in data.items()}
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def build_cast(
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world: World,
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thread: Thread,
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episodes: list[Episode],
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*,
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gateway: Gateway | None = None,
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with_bios: bool = True,
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) -> tuple[str, list[CastRow], list[factcheck.Suspicion]]:
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"""返回 (Markdown 文本, 表格行, 小传里查出的可疑专名)。"""
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rows = collect_rows(world, thread, episodes)
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events = [e for ep in episodes for e in ep.events] or thread.events
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bios: dict[str, str] = {}
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if with_bios and rows:
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reply = chat(
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[{"role": "user", "content": _bio_prompt(world, rows, events)}],
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gateway=gateway,
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max_tokens=MAX_TOKENS_STRUCTURED,
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)
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bios = _parse_bios(reply.content)
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suspicions: list[factcheck.Suspicion] = []
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bios_text = "\n".join(f"{k}：{v}" for k, v in bios.items())
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if bios_text:
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suspicions = factcheck.check(bios_text, world)
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lines = [
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f"# {world.name} · 人物表",
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"",
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f"本卷主角线索：{'、'.join(world.figure_name(m) for m in thread.members)}",
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f"线索跨度：{_span_of(thread, episodes)}",
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"",
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"| 人物 | 种族 | 生卒 | 身份 | 本卷出场 | 角色 |",
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"|---|---|---|---|---|---|",
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]
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lines.extend(row.as_row() for row in rows)
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if bios:
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lines += ["", "## 小传", ""]
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for row in rows:
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bio = bios.get(row.name) or bios.get(row.name.lower())
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if bio:
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lines.append(f"**{row.name}** —— {bio}")
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lines.append("")
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if suspicions:
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lines += [
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"",
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"> ⚠️ 小传中以下专名未在史料中找到，可能是演绎时误引入："
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+ "、".join(f"{s.name}×{s.count}" for s in suspicions[:8]),
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"",
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]
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return "\n".join(lines) + "\n", rows, suspicions
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def write_cast(path: Path, text: str) -> Path:
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(text, encoding="utf-8")
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return path
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