Triple

T14785464
Position Surface form Disambiguated ID Type / Status
Subject Martín (Hache) E347511 entity
Predicate screenwriter P2831 FINISHED
Object Kathy Saavedra E1239685 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: Kathy Saavedra | Statement: [Martín (Hache), screenwriter, Kathy Saavedra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kathy Saavedra
Context triple: [Martín (Hache), screenwriter, Kathy Saavedra]
  • A. Kathy Saavedra chosen
    Kathy Saavedra is a screenwriter best known for her work on the film "Un lugar en el mundo."
  • B. Kathleen Avila
    Kathleen Avila is a fictional character appearing in the crime drama film "Internal Affairs."
  • C. Lisa Benavides
    Lisa Benavides is an American actress known for her work in independent films and as the wife of actor-director Tim Blake Nelson.
  • D. Teresa Mendoza
    Teresa Mendoza is a resilient Mexican woman who rises from a poor background to become a powerful drug cartel leader in the crime drama "Queen of the South."
  • E. Brenda Serrano
    Brenda Serrano is the central protagonist of the television series "My Generation," around whose life and experiences the show's narrative primarily revolves.
  • 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_69d822e9b9e08190bedcc31a163fda82 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deca9f1c9c8190a8b28ba0ddd3e2e3 completed April 14, 2026, 11:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00cfb725d48190bdca0a85ca7f440c completed May 10, 2026, 6:34 p.m.
Created at: April 10, 2026, 1:31 a.m.