Triple

T10031694
Position Surface form Disambiguated ID Type / Status
Subject 1950 Academy Awards E204866 entity
Predicate bestCinematographyBlackAndWhiteWinner P37206 FINISHED
Object Battleground E136812 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: Battleground | Statement: [1950 Academy Awards, bestCinematographyBlackAndWhiteWinner, Battleground]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Battleground
Context triple: [1950 Academy Awards, bestCinematographyBlackAndWhiteWinner, Battleground]
  • A. Battleground chosen
    "Battleground" is a World War II drama film written by Paul Vogel that follows a besieged American infantry unit during the Battle of the Bulge.
  • B. Batalha
    Batalha is a Portuguese town best known for its UNESCO-listed Batalha Monastery, a masterpiece of Gothic and Manueline architecture.
  • C. Battles
    Battles is an American experimental rock band known for its complex, rhythmically intricate compositions and innovative use of looping and electronics.
  • D. Battle for Land
    Battle for Land was a Fascist Italy agricultural and land reclamation initiative aimed at increasing arable land and showcasing the regime’s economic and ideological strength.
  • E. Battlefield Earth
    Battlefield Earth is a 2000 science fiction film, based on L. Ron Hubbard’s novel, widely known for its critical and commercial failure and frequent citation as one of the worst movies ever made.
  • 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_69ca834d77188190ad645e33e8ca3200 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdce461d6481908cc8f968856e0337 completed April 2, 2026, 2:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2823e63488190bef7633b1a755df8 completed April 5, 2026, 3:39 p.m.
Created at: March 30, 2026, 8:54 p.m.