Triple

T5277911
Position Surface form Disambiguated ID Type / Status
Subject Stanford Theatre E119417 entity
Predicate hasConcession P17433 FINISHED
Object traditional movie snacks 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: traditional movie snacks | Statement: [Stanford Theatre, hasConcession, traditional movie snacks]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasConcession
Context triple: [Stanford Theatre, hasConcession, traditional movie snacks]
  • A. hasConcessions chosen
    Indicates that one entity provides or contains concession facilities, services, or rights (such as food, drink, or merchandise sales) for another entity or within a given context.
  • B. hasConcessionaire
    Indicates that one entity is designated as the concessionaire (holder of operating or usage rights under a concession) for another entity.
  • C. concessionType
    Indicates the specific kind or category of concession (such as a discount, exemption, or special allowance) that applies in a given context.
  • D. concessionExtended
    Indicates that one party has granted or prolonged a special allowance, discount, or favorable term to another party.
  • E. concessionStart
    Indicates the point in a discourse or text where a concession begins, marking a shift to acknowledging a contrasting or limiting consideration relative to what was previously stated.
  • 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_69bd446d05a8819092ad333a3f9c8d5c completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd8c9c72b08190947b6b955ac1bb5a completed March 20, 2026, 6:06 p.m.
PD Predicate disambiguation batch_69bd844a56b48190ad743c42246e02dd completed March 20, 2026, 5:30 p.m.
Created at: March 20, 2026, 1:51 p.m.