Triple

T30177925
Position Surface form Disambiguated ID Type / Status
Subject Fancheng District E767112 entity
Predicate hasAdministrativeCenter P1474 FINISHED
Object Fancheng District government
Fancheng District government is the primary local administrative authority responsible for governing and managing public affairs in Fancheng District.
E1902709 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: Fancheng District government | Statement: [Fancheng District, hasAdministrativeCenter, Fancheng District government]
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: Fancheng District government
Triple: [Fancheng District, hasAdministrativeCenter, Fancheng District government]
Generated description
Fancheng District government is the primary local administrative authority responsible for governing and managing public affairs in Fancheng District.

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_69f2247ba20c81909d34f2bfed706e1e completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67f3f6930819088f6bb2c24573ebb completed May 2, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27586677c48190869c43655cf24a32 completed June 9, 2026, 12:03 a.m.
NEDg Description generation batch_6a275a4311f08190b067b8c94e48d019 completed June 9, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_6a275aeeed3c8190ba20d38ec0af1c74 completed June 9, 2026, 12:14 a.m.
Created at: April 29, 2026, 7:25 p.m.