Triple

T12794301
Position Surface form Disambiguated ID Type / Status
Subject Adrian Grunberg E305849 entity
Predicate workedOn P3 FINISHED
Object Get the Gringo E1003174 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: Get the Gringo | Statement: [Adrian Grunberg, workedOn, Get the Gringo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Get the Gringo
Context triple: [Adrian Grunberg, workedOn, Get the Gringo]
  • A. Get the Gringo chosen
    Get the Gringo is a 2012 action crime film starring Mel Gibson as a career criminal navigating the dangers of a corrupt Mexican prison.
  • B. Gringo
    "Gringo" is a popular dancehall song by Ghanaian artist Shatta Wale, known for its catchy hook and cinematic Western-themed music video.
  • C. Gringo
    Gringo is a dark comedy–crime film in which Paris Jackson appears alongside an ensemble cast in a story about a businessman caught up in a dangerous scheme in Mexico.
  • D. GRO
    GRO is the FAA airport code assigned to Rota International Airport, a public airport serving the island of Rota in the Northern Mariana Islands.
  • E. GRO
    GRO is the station code used to identify Grove Street station on the Newark Light Rail system in New Jersey.
  • 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_69d7bdf366888190a8cccb982606889c completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96e6ca0288190aba01735b71a01da completed April 10, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69b9418b08190a61ee4ec4283767c completed May 3, 2026, 12:49 a.m.
Created at: April 9, 2026, 5:30 p.m.