Triple

T27058577
Position Surface form Disambiguated ID Type / Status
Subject Guicán E684970 entity
Predicate category P87 FINISHED
Object Municipalities of Boyacá Department
Municipalities of Boyacá Department are the local administrative divisions within the Boyacá Department of Colombia, each governed by its own municipal authorities.
E1706858 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: Municipalities of Boyacá Department | Statement: [Guicán, category, Municipalities of Boyacá Department]
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: Municipalities of Boyacá Department
Triple: [Guicán, category, Municipalities of Boyacá Department]
Generated description
Municipalities of Boyacá Department are the local administrative divisions within the Boyacá Department of Colombia, each governed by its own municipal authorities.

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_69ef14829fac8190914bef9ecc3005d7 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f622e297ac8190b37c546e863e016e completed May 2, 2026, 4:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123acea7208190978f664e8000af6c completed May 23, 2026, 11:39 p.m.
NEDg Description generation batch_6a123bd93ac081909b060b395b1d3e81 completed May 23, 2026, 11:44 p.m.
NED2 Entity disambiguation (via description) batch_6a123c4f67388190a885b5ce89f9baa6 completed May 23, 2026, 11:46 p.m.
Created at: April 27, 2026, 8:19 a.m.