Triple

T26696624
Position Surface form Disambiguated ID Type / Status
Subject Petrolacosaurus kansensis E673038 entity
Predicate hasTemporalFenestration P160467 FINISHED
Object two lateral temporal openings 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: two lateral temporal openings | Statement: [Petrolacosaurus kansensis, hasTemporalFenestration, two lateral temporal openings]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasTemporalFenestration
Context triple: [Petrolacosaurus kansensis, hasTemporalFenestration, two lateral temporal openings]
  • A. hasTemporalResolution
    Indicates that one entity specifies the level of temporal detail or granularity at which another entity’s data, observation, or process is measured or represented.
  • B. temporalFenestraCountPerSide chosen
    Indicates the number of temporal fenestra openings present on each side of the skull.
  • C. hasTemporalBehavior
    Indicates that an entity exhibits a particular pattern, characteristic, or change in behavior over time.
  • D. hasTemporalUse
    Indicates that something is used, applicable, or valid only during a specific time or temporal interval.
  • E. hasTemporalClassification
    Indicates a relationship where something is assigned or associated with a specific temporal category, period, or time-based classification.
  • 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_69eecda2b49c8190a6c481cfc4c07954 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f6afebd7ec8190ab696f363d84abf0 completed May 3, 2026, 2:16 a.m.
PD Predicate disambiguation batch_69f6aca204148190850a3dc325bc07b7 completed May 3, 2026, 2:02 a.m.
Created at: April 27, 2026, 3:29 a.m.