Triple

T20492785
Position Surface form Disambiguated ID Type / Status
Subject 富春江 E502787 entity
Predicate hasAttraction P105 FINISHED
Object 富春江小三峡 NE NERFINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 富春江小三峡 | Statement: [富春江, hasAttraction, 富春江小三峡]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 富春江小三峡
Context triple: [富春江, hasAttraction, 富春江小三峡]
  • A. 富春江 chosen
    富春江是中国浙江省境内一条以秀丽山水风光和人文景观著称的重要河流与旅游胜地。
  • B. 巫峡
    巫峡是中国长江三峡中以幽深秀丽景色和神话传说著称的峡谷。
  • C. 上川
    上川是位于中国广东省台山市附近海域的一座海岛,以渔业资源和海滨旅游景观而闻名。
  • D. 龙庆峡
    龙庆峡是位于北京市延庆区、以峡谷风光和冬季冰灯艺术节闻名的著名自然风景旅游区。
  • E. 杭州苏堤
    杭州苏堤是位于杭州西湖上的著名堤岸景观,以春日“苏堤春晓”美景闻名于世。
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69e0b4b0373881909dd3e9387f82eab4 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e69cbb3bd081909351525208b41bba completed April 20, 2026, 9:38 p.m.
Created at: April 16, 2026, 11:35 a.m.