Triple

T24821629
Position Surface form Disambiguated ID Type / Status
Subject municipal government of Camocim E621077 entity
Predicate jurisdiction P82 FINISHED
Object municipality of Camocim
The municipality of Camocim is a coastal city and administrative region in the state of Ceará, Brazil, known for its beaches, fishing activities, and local tourism.
E621077 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 Camocim | Statement: [municipal government of Camocim, jurisdiction, municipality of Camocim]
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 Camocim
Triple: [municipal government of Camocim, jurisdiction, municipality of Camocim]
Generated description
The municipality of Camocim is a coastal city and administrative region in the state of Ceará, Brazil, known for its beaches, fishing activities, and local tourism.

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_69e2fabfd4648190bd0e5c7f4dbb6cab completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f42298c7208190ab487b4dfcd1960b completed May 1, 2026, 3:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101c3fe7908190840e6e31eab5abbc completed May 22, 2026, 9:05 a.m.
NEDg Description generation batch_6a1027e435748190a727eb58546d634f completed May 22, 2026, 9:54 a.m.
NED2 Entity disambiguation (via description) batch_6a1028f3eb648190a16d7ad44a76aa54 completed May 22, 2026, 9:59 a.m.
Created at: April 18, 2026, 5:04 a.m.