Triple

T31421720
Position Surface form Disambiguated ID Type / Status
Subject Yantan District E801547 entity
Predicate hasCapital P204 FINISHED
Object Yantan District government seat
Yantan District government seat is the main administrative center where the local government of Yantan District conducts its official functions and governance.
E801547 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: Yantan District government seat | Statement: [Yantan District, hasCapital, Yantan District government seat]
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: Yantan District government seat
Triple: [Yantan District, hasCapital, Yantan District government seat]
Generated description
Yantan District government seat is the main administrative center where the local government of Yantan District conducts its official functions and governance.

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_69f348c26f048190b4adadd71b4596c5 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a0bd0a388190aec989e005870bd6 completed May 3, 2026, 1:11 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ad25c5e848190a93000961ead6847 completed June 11, 2026, 3:21 p.m.
NEDg Description generation batch_6a2ad44d28f881909df438c214114a1a completed June 11, 2026, 3:29 p.m.
NED2 Entity disambiguation (via description) batch_6a2ae4a20778819094804989ecec8954 completed June 11, 2026, 4:38 p.m.
Created at: April 30, 2026, 8:49 p.m.