Triple

T36228692
Position Surface form Disambiguated ID Type / Status
Subject Yajalón E891178 entity
Predicate governingBody P46 FINISHED
Object municipal government of Yajalón
The municipal government of Yajalón is the local public administration responsible for managing services, development, and regulations within the municipality of Yajalón in Mexico.
E2174423 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: municipal government of Yajalón | Statement: [Yajalón, governingBody, municipal government of Yajalón]
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: municipal government of Yajalón
Triple: [Yajalón, governingBody, municipal government of Yajalón]
Generated description
The municipal government of Yajalón is the local public administration responsible for managing services, development, and regulations within the municipality of Yajalón in Mexico.

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_69f76e4387048190a1b27bcbf4ec7423 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b5a0d2a48190a32496970ed5f223 completed May 3, 2026, 8:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a394d38566c8190bd66d416bdebb489 completed June 22, 2026, 2:56 p.m.
NEDg Description generation batch_6a394dff6b688190a7bcb867748bc0fe completed June 22, 2026, 3 p.m.
NED2 Entity disambiguation (via description) batch_6a394e7505d08190b3d191cefe3d2233 completed June 22, 2026, 3:02 p.m.
Created at: May 3, 2026, 4:09 p.m.