Triple

T11578538
Position Surface form Disambiguated ID Type / Status
Subject Fables E274565 entity
Predicate basedOn P98 FINISHED
Object Aesopic fables E314182 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: Aesopic fables | Statement: [Fables, basedOn, Aesopic fables]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aesopic fables
Context triple: [Fables, basedOn, Aesopic fables]
  • A. Aesop's fables chosen
    Aesop's fables are a classic collection of short moral stories, traditionally attributed to the ancient Greek storyteller Aesop, that use animals and everyday situations to illustrate ethical lessons.
  • B. Fables
    Fables is a collection of satirical verse tales by John Gay that use animal characters and moral lessons to comment on human nature and society.
  • C. Fables
    Fables is a collection of medieval verse tales by Marie de France that adapt and moralize traditional animal stories and folktales.
  • D. Fables
    Fables is a comic book series created by Bill Willingham that reimagines classic fairy-tale and folklore characters living in exile in modern-day New York City.
  • E. La Fontaine’s Fables
    La Fontaine’s Fables is a classic 17th-century collection of moral tales in verse by French poet Jean de La Fontaine, featuring animals and humans to satirize society and human nature.
  • 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_69d6aae5ac3c81908d2b0a3a665665b2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8904b46288190890ecafd6ceb0c3d completed April 10, 2026, 5:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69e714080a60819095205355776c8637 completed April 21, 2026, 6:07 a.m.
Created at: April 8, 2026, 9:38 p.m.