Triple

T27959520
Position Surface form Disambiguated ID Type / Status
Subject Baidi E704540 entity
Predicate hasLandmark P105 FINISHED
Object Baidi Temple
Baidi Temple is a historic riverside temple complex in Fengjie County, Chongqing, China, famed for its association with ancient poets and its scenic views over the Yangtze River and the Three Gorges.
E1812065 NE FINISHED

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: Baidi Temple | Statement: [Baidi, hasLandmark, Baidi Temple]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Baidi Temple
Triple: [Baidi, hasLandmark, Baidi Temple]
Generated description
Baidi Temple is a historic riverside temple complex in Fengjie County, Chongqing, China, famed for its association with ancient poets and its scenic views over the Yangtze River and the Three Gorges.

Provenance (5 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_69ef841061e48190b5570f9562f7434d completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f63b0218808190b3e543cc1a0bb5cb completed May 2, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1606f4c08881909e3a8650b2bfbf72 completed May 26, 2026, 8:47 p.m.
NEDg Description generation batch_6a1612e18e8c8190b09f4603345ef0c7 completed May 26, 2026, 9:38 p.m.
NED2 Entity disambiguation (via description) batch_6a1613c4f4908190bdf67095802e7e75 completed May 26, 2026, 9:42 p.m.
Created at: April 27, 2026, 7:30 p.m.