Triple

T4256591
Position Surface form Disambiguated ID Type / Status
Subject Gilão River E95988 entity
Predicate nearbyTown P3883 FINISHED
Object Santa Luzia E387822 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: Santa Luzia | Statement: [Gilão River, nearbyTown, Santa Luzia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Santa Luzia
Context triple: [Gilão River, nearbyTown, Santa Luzia]
  • A. Santa Luzia chosen
    Santa Luzia is a small fishing village in Portugal’s Algarve region, known for its traditional octopus fishing and tranquil coastal atmosphere.
  • B. Santa Luzia
    Santa Luzia is a civil parish within the municipality of Angra do Heroísmo on Terceira Island in Portugal’s Azores archipelago.
  • C. Santa Isabel
    Santa Isabel was the colonial capital city of Spanish Equatorial Guinea, serving as the administrative and political center during Spanish rule.
  • D. Santo Antônio
    Santo Antônio is a historic central neighborhood of Recife, Brazil, known for its colonial architecture, commercial activity, and cultural landmarks.
  • E. San-São
    San-São is the traditional Brazilian football derby between São Paulo FC and Santos FC, known for its historic rivalries and memorable matches.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69b3454095ac81909c2494f7ff294af1 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34ec1971c81908f7a72418efa8bcc completed March 12, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a88a68f081909e5bae5b0414f534 completed March 14, 2026, 6:27 p.m.
Created at: March 12, 2026, 11:06 p.m.