Triple

T9695862
Position Surface form Disambiguated ID Type / Status
Subject Fed. 1 E234647 entity
Predicate connectsTo P845 FINISHED
Object Guerrero Negro E234648 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: Guerrero Negro | Statement: [Fed. 1, connectsTo, Guerrero Negro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guerrero Negro
Context triple: [Fed. 1, connectsTo, Guerrero Negro]
  • A. Guerrero Negro chosen
    Guerrero Negro is a town in Baja California Sur, Mexico, best known for its large salt production facilities and as a prime destination for gray whale watching.
  • B. Guerrero
    Guerrero is a coastal state in southwestern Mexico known for its mountainous terrain, including part of the Sierra Madre del Sur, and popular tourist destinations such as Acapulco.
  • C. Guerrero
    Guerrero is a Mexico City neighborhood and metro station area known for its central location and connectivity within the capital’s transit system.
  • D. Guerrero
    Guerrero is a resourceful and enigmatic former government operative turned private investigator’s associate from the television series "Human Target."
  • E. Amarildo
    Amarildo is a former Brazilian footballer best known as a forward who starred for Botafogo and the Brazil national team in the early 1960s.
  • 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9d366c488190bc153c68fef197c2 completed April 1, 2026, 10:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1912216d481909f6a0f977d570a93 completed April 4, 2026, 10:30 p.m.
Created at: March 30, 2026, 8:17 p.m.