Triple

T34774596
Position Surface form Disambiguated ID Type / Status
Subject Huancayo District E1002466 entity
Predicate governingBody P46 FINISHED
Object Municipality of Huancayo
The Municipality of Huancayo is the local government authority responsible for administering public services, urban planning, and development policies in the city and district of Huancayo in Peru.
E2111415 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: Municipality of Huancayo | Statement: [Huancayo District, governingBody, Municipality of Huancayo]
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: Municipality of Huancayo
Triple: [Huancayo District, governingBody, Municipality of Huancayo]
Generated description
The Municipality of Huancayo is the local government authority responsible for administering public services, urban planning, and development policies in the city and district of Huancayo in Peru.

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_69f76db30a108190bb57ca95b873e5bb completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f77a3d09d88190a35d4eb43c297a56 completed May 3, 2026, 4:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37663d53348190a9af7b6f60828114 completed June 21, 2026, 4:19 a.m.
NEDg Description generation batch_6a376786393881908174db360401e0f3 completed June 21, 2026, 4:24 a.m.
NED2 Entity disambiguation (via description) batch_6a37685b8d548190a423019edcab9bc8 completed June 21, 2026, 4:28 a.m.
Created at: May 3, 2026, 3:59 p.m.