Triple

T34278060
Position Surface form Disambiguated ID Type / Status
Subject 西城区 E879515 entity
Predicate hasLandmark P105 FINISHED
Object 恭王府（靠近什刹海）
恭王府（靠近什刹海）是北京保存最完整、规模最大的清代王府之一，以精美的园林建筑和深厚的历史文化底蕴著称。
 E2090190 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: 恭王府（靠近什刹海） | Statement: [西城区, hasLandmark, 恭王府（靠近什刹海）]
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: 恭王府（靠近什刹海）
Triple: [西城区, hasLandmark, 恭王府（靠近什刹海）]
Generated description
恭王府（靠近什刹海）是北京保存最完整、规模最大的清代王府之一，以精美的园林建筑和深厚的历史文化底蕴著称。

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_69f349b5f6648190b9420d94a4cd16e0 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f712ec33088190b4e1c5fa63d07db0 completed May 3, 2026, 9:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36e629ba608190b3289611cf455612 completed June 20, 2026, 7:12 p.m.
NEDg Description generation batch_6a36e7c3a5d08190a90848fbc53f577a completed June 20, 2026, 7:19 p.m.
NED2 Entity disambiguation (via description) batch_6a36e8214050819088ce2baa27717cdf completed June 20, 2026, 7:21 p.m.
Created at: May 1, 2026, 1:57 a.m.