Triple

T4106583
Position Surface form Disambiguated ID Type / Status
Subject NATO Land Command E88465 entity
Predicate abbreviation P43 FINISHED
Object LANDCOM E412315 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: LANDCOM | Statement: [NATO Land Command, abbreviation, LANDCOM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LANDCOM
Context triple: [NATO Land Command, abbreviation, LANDCOM]
  • A. LANDCOM chosen
    LANDCOM is NATO’s headquarters responsible for overseeing and coordinating the alliance’s land forces operations and readiness.
  • B. Volacom
    Volacom is a company founded by Tesla co-founder and battery technology pioneer JB Straubel, likely focused on advanced engineering and sustainable technology solutions.
  • C. Metricom
    Metricom was a pioneering wireless data communications company best known for its Ricochet wireless internet service in the 1990s.
  • D. Erlecom
    Erlecom is a small village in the Dutch province of Gelderland, situated along the Waal River within the municipality of Berg en Dal.
  • E. Comnidyne
    Comnidyne is the fictional corporation where Kevin Spacey’s character Dave Harken works as a tyrannical boss in the comedy film "Horrible Bosses."
  • 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_69aed9484fb881909146f4c772ad277c completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af019c7a3c8190a503ce80e87dc3b3 completed March 9, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b576a695b08190890aa4f64289d227 completed March 14, 2026, 2:54 p.m.
Created at: March 9, 2026, 3:40 p.m.