Triple

T6039078
Position Surface form Disambiguated ID Type / Status
Subject Arlington International Racecourse E134495 entity
Predicate reasonForRebuild P50914 FINISHED
Object fire destroyed grandstand in 1985 LITERAL 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: fire destroyed grandstand in 1985 | Statement: [Arlington International Racecourse, reasonForRebuild, fire destroyed grandstand in 1985]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: reasonForRebuild
Context triple: [Arlington International Racecourse, reasonForRebuild, fire destroyed grandstand in 1985]
  • A. rebuild
    Indicates restoring or constructing again something that was previously built, often after damage, destruction, or significant alteration.
  • B. reasonForNewBuilding chosen
    Indicates the underlying cause, motivation, or justification for constructing a new building.
  • C. majorRebuilder
    Indicates that an entity plays a primary or leading role in reconstructing, restoring, or significantly rebuilding another entity.
  • D. reasonForRedesign
    Indicates the underlying cause, motivation, or justification for performing a redesign.
  • E. rebuiltFor
    Indicates that one entity has been reconstructed, renovated, or modified specifically to serve the needs, purposes, or use of another entity.
  • F. None of above.

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_69c00875db5c819099dd5bb833ec43c2 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c056ce10cc8190817ade56570adc92 completed March 22, 2026, 8:53 p.m.
PD Predicate disambiguation batch_69c049e9a68c81909da0cfe4779ce9b5 completed March 22, 2026, 7:58 p.m.
Created at: March 22, 2026, 4:08 p.m.