Triple

T37411691
Position Surface form Disambiguated ID Type / Status
Subject Beauty and the Beast (traditional fairy tale) E929586 entity
Predicate hasEarliestKnownLiteraryVersion P188319 FINISHED
Object La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve
La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve is an 18th-century French literary fairy tale that presents an elaborate, adult-oriented version of the Beauty and the Beast story, rich in backstory and courtly detail.
 E929586 NE FINISHED

How this triple was built (3 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: La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve | Statement: [Beauty and the Beast (traditional fairy tale), hasEarliestKnownLiteraryVersion, La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve
Triple: [Beauty and the Beast (traditional fairy tale), hasEarliestKnownLiteraryVersion, La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve]
Generated description
La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve is an 18th-century French literary fairy tale that presents an elaborate, adult-oriented version of the Beauty and the Beast story, rich in backstory and courtly detail.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasEarliestKnownLiteraryVersion
Context triple: [Beauty and the Beast (traditional fairy tale), hasEarliestKnownLiteraryVersion, La Belle et la Bête by Gabrielle‑Suzanne Barbot de Villeneuve]
  • A. hasAncientLiteraryTradition
    Indicates that an entity possesses a long-established, historically significant body of written literature originating in ancient times.
  • B. firstWellKnownLiteraryVersionYear
    Indicates the year in which the first well-known literary version of something (such as a story, character, or motif) was published or recorded.
  • C. earliestTextsIn
    Indicates that certain texts are among the earliest known examples found in or associated with a particular place or context.
  • D. hasWritingTraditionSince
    Indicates that a writing tradition has been present or established for an entity starting from a specified point in time.
  • E. hasEarliestMajorCorpus
    Indicates that one entity is associated with the earliest significant or primary body of work (major corpus) relative to other comparable entities.
  • F. None of above. chosen

Provenance (7 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_69f76ebde49481908566cd96b37ccc84 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba68077788190b311e027435fcf87 completed May 6, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a407711cfcc819082372a9033677f01 completed June 28, 2026, 1:21 a.m.
NEDg Description generation batch_6a407841ee948190b45372dab14ea45f completed June 28, 2026, 1:26 a.m.
NED2 Entity disambiguation (via description) batch_6a40791b81a481908283707caf3d7394 completed June 28, 2026, 1:30 a.m.
PD Predicate disambiguation batch_69fba34c65ac8190b298f0f00d1dcc0e completed May 6, 2026, 8:23 p.m.
PDg Predicate description generation batch_69fba67f78348190ab160988e4698394 completed May 6, 2026, 8:37 p.m.
Created at: May 3, 2026, 4:16 p.m.