Triple

T9632248
Position Surface form Disambiguated ID Type / Status
Subject Flora Lamson Hewlett E232835 entity
Predicate givenName P17 FINISHED
Object Flora E712737 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: Flora | Statement: [Flora Lamson Hewlett, givenName, Flora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flora
Context triple: [Flora Lamson Hewlett, givenName, Flora]
  • A. Flora
    Flora is a celebrated 19th-century botanical illustration by Mary Evelyn Pickering that showcases her detailed and artistic rendering of plant life.
  • B. Flora
    Flora is a symbolist painting by Evelyn De Morgan depicting the Roman goddess of flowers and spring in a richly allegorical, Pre-Raphaelite-inspired style.
  • C. Flora
    Flora is the young niece in Henry James's novella "The Turn of the Screw," whose eerie innocence and ambiguous relationship to the supernatural are central to the story's psychological horror.
  • D. Flora
    Flora is the middle name of Ruth Disney, the daughter of Walt Disney and his wife Lillian.
  • E. Flora chosen
    Flora is a feminine given name of Latin origin meaning "flower," historically associated with the Roman goddess of flowers and spring.
  • 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_69ca848940cc8190b97cec654cb3bb4a completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9b2783b48190a9929dc3e3cd2956 completed April 1, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18232e34c8190a19685ee9210e88b completed April 4, 2026, 9:27 p.m.
Created at: March 30, 2026, 8:11 p.m.