Triple

T1775901
Position Surface form Disambiguated ID Type / Status
Subject Syracuse Orange football E38977 entity
Predicate nickname P55 FINISHED
Object Orange E3952 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: Orange | Statement: [Syracuse Orange football, nickname, Orange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orange
Context triple: [Syracuse Orange football, nickname, Orange]
  • A. Orange
    Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
  • B. Orange chosen
    Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
  • C. Orange
    Orange is a regional city in the Central Tablelands of New South Wales, Australia, known for its cool-climate wines, agriculture, and growing tourism industry.
  • D. Maroon
    Maroon refers to the descendants of escaped African slaves in the Americas who formed independent communities, notably in places like Suriname and Jamaica, preserving distinct African-derived cultures and traditions.
  • E. Brown
    Brown is a common English-language surname of Anglo-Saxon origin, typically derived from a nickname referring to hair color, complexion, or clothing.
  • 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_69a8862e61708190af97b9838cc3f5de completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64b839608190b32bc041267458d5 completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada99a81c08190b602858708263193 completed March 8, 2026, 4:53 p.m.
Created at: March 4, 2026, 7:31 p.m.