Triple

T23754691
Position Surface form Disambiguated ID Type / Status
Subject PARNA Serra de Itabaiana E587072 entity
Predicate locatedNear P294 FINISHED
Object city of Itabaiana
The city of Itabaiana is a municipality in the state of Sergipe, Brazil, known as a regional commercial hub and gateway to the nearby Serra de Itabaiana National Park.
E1599809 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: city of Itabaiana | Statement: [PARNA Serra de Itabaiana, locatedNear, city of Itabaiana]
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: city of Itabaiana
Triple: [PARNA Serra de Itabaiana, locatedNear, city of Itabaiana]
Generated description
The city of Itabaiana is a municipality in the state of Sergipe, Brazil, known as a regional commercial hub and gateway to the nearby Serra de Itabaiana National Park.

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_69e2490a0eec81908cdef8a862828d7a completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1bcc561b081908bfd516ef5e2dcfd completed April 29, 2026, 8:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f53d7ecdc8190942aa11cec5a1690 completed May 21, 2026, 6:50 p.m.
NEDg Description generation batch_6a0f54af0b108190a7c3e0ac0f48aabf completed May 21, 2026, 6:53 p.m.
NED2 Entity disambiguation (via description) batch_6a0f5587b89481908744d12128349956 completed May 21, 2026, 6:57 p.m.
Created at: April 17, 2026, 7:13 p.m.