Triple

T8506752
Position Surface form Disambiguated ID Type / Status
Subject Flora (painting) E201351 entity
Predicate title P38 FINISHED
Object Flora E201351 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 (painting), title, Flora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flora
Context triple: [Flora (painting), title, 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 chosen
    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
    Flora is one of the three good fairies in Disney's "Sleeping Beauty," known for her red attire, leadership among the fairies, and role in protecting Princess Aurora.
  • 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_69ca831fe47c8190b5c57b456d2aefa0 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe5db51e4819098dde316e87e8b0d completed March 31, 2026, 3:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6d257a748190b33873ee0d252d2e completed April 2, 2026, 1:20 p.m.
Created at: March 30, 2026, 6:14 p.m.