Triple

T14999188
Position Surface form Disambiguated ID Type / Status
Subject AF 8x-xxxx E374037 entity
Predicate uniquenessScope P39874 FINISHED
Object unique to each individual B-2 aircraft 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: unique to each individual B-2 aircraft | Statement: [AF 8x-xxxx, uniquenessScope, unique to each individual B-2 aircraft]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: uniquenessScope
Context triple: [AF 8x-xxxx, uniquenessScope, unique to each individual B-2 aircraft]
  • A. uniquenessCondition
    Indicates that a specified element, value, or combination of attributes must be unique within a given set, context, or domain, with no duplicates allowed.
  • B. typeOfUniqueness
    Indicates that one entity’s uniqueness is characterized, classified, or constrained by the specific kind or mode of uniqueness associated with another entity.
  • C. isUniqueTo chosen
    Indicates that a property, characteristic, or association belongs exclusively to a particular entity and is not shared with any other.
  • D. hasUniqueness
    Indicates that something possesses a distinctive or one-of-a-kind quality that sets it apart from others.
  • E. reservationScope
    Indicates the extent or range within which a reservation applies or is valid.
  • 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_69d85ccc84388190aa151e5173370c8d completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded71a5618819083ae96a79735ef98 completed April 15, 2026, 12:08 a.m.
PD Predicate disambiguation batch_69de9a6169b48190a679609febd2d0e3 completed April 14, 2026, 7:49 p.m.
Created at: April 10, 2026, 2:54 a.m.