Triple

T11699131
Position Surface form Disambiguated ID Type / Status
Subject Paul Clifford E278073 entity
Predicate hasLoveStory P85591 FINISHED
Object romance between Paul Clifford and Lucy Brandon 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: romance between Paul Clifford and Lucy Brandon | Statement: [Paul Clifford, hasLoveStory, romance between Paul Clifford and Lucy Brandon]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasLoveStory
Context triple: [Paul Clifford, hasLoveStory, romance between Paul Clifford and Lucy Brandon]
  • A. hasLoveLifeCharacteristic
    Indicates that an entity possesses a particular quality, status, or attribute related to its romantic or love life.
  • B. hasMarriagePlot
    Indicates that the work’s narrative centrally involves courtship, romantic relationships, or the progression toward marriage as a key plot element.
  • C. hasSpouseInStory
    Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
  • D. hasFictionalBeloved chosen
    Indicates that an entity has a romantic partner or beloved who exists only as a fictional character.
  • E. hasAllyInStory
    Indicates that one entity is portrayed as an ally or supportive partner of another entity within the context of a specific story or narrative.
  • 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_69d6aafe02d881909900d54ad7d4af84 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a47df68c81908a91919a69b4880d completed April 10, 2026, 7:19 a.m.
PD Predicate disambiguation batch_69d88a7b30948190b616a9db5c5488d5 completed April 10, 2026, 5:28 a.m.
Created at: April 8, 2026, 9:40 p.m.