Triple

T15047894
Position Surface form Disambiguated ID Type / Status
Subject Freddie Baxter E379277 entity
Predicate romanticInvolvementType P82370 FINISHED
Object casual relationship with Henry Best 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: casual relationship with Henry Best | Statement: [Freddie Baxter, romanticInvolvementType, casual relationship with Henry Best]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: romanticInvolvementType
Context triple: [Freddie Baxter, romanticInvolvementType, casual relationship with Henry Best]
  • A. romanticRelationshipStatus chosen
    Indicates the nature or state of a romantic relationship between entities, such as whether they are dating, committed, separated, or otherwise romantically involved.
  • B. relationshipOutcome
    Indicates the result or consequence that arises from a particular relationship between two or more entities.
  • C. relationshipDevelopsWith
    Indicates that a relationship grows, evolves, or becomes more developed between two entities over time.
  • D. relationshipStatusDuringFilm
    Indicates the type or state of a relationship between entities specifically during the time period in which a film takes place or is produced.
  • E. relationshipDurationWith
    Indicates the length of time that a specified relationship between two entities has existed or is expected to last.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69deda8e64e48190873104a02a676ff3 completed April 15, 2026, 12:23 a.m.
PD Predicate disambiguation batch_69de9a69d7848190b2b4662dd30f20e9 completed April 14, 2026, 7:50 p.m.
Created at: April 10, 2026, 3 a.m.