Triple

T22708524
Position Surface form Disambiguated ID Type / Status
Subject The String Cheese Incident E561527 entity
Predicate basedIn P40 FINISHED
Object Colorado NE NERFINISHED

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: Colorado | Statement: [The String Cheese Incident, basedIn, Colorado]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Colorado
Context triple: [The String Cheese Incident, basedIn, Colorado]
  • A. Colorado chosen
    Colorado is a landlocked U.S. state known for its Rocky Mountain landscapes, outdoor recreation, and cities like Denver and Boulder.
  • B. Colorado
    Colorado is a Brazilian football club nickname commonly used for Sport Club Internacional, referencing the team's traditional red colors.
  • C. Colorado
    Colorado is a Barbacoan language spoken by indigenous communities in parts of Colombia and Ecuador.
  • D. Como, Colorado
    Como, Colorado is a small historic unincorporated community and former railroad town located in the high plains of central Colorado.
  • E. D. Colo.
    D. Colo. is the standard legal abbreviation for the United States District Court for the District of Colorado, a federal trial court within the Tenth Circuit.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2454f1348819088d83f420925a5c1 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f178d11d4081909981872698b6c45e completed April 29, 2026, 3:19 a.m.
Created at: April 17, 2026, 3:17 p.m.