Triple

T12060915
Position Surface form Disambiguated ID Type / Status
Subject Don Bluth E287165 entity
Predicate directed P7373 FINISHED
Object Thumbelina E630016 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: Thumbelina | Statement: [Don Bluth, directed, Thumbelina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thumbelina
Context triple: [Don Bluth, directed, Thumbelina]
  • A. Thumbelina chosen
    Thumbelina is a classic fairy tale about a tiny girl and her adventures, written by Danish author Hans Christian Andersen.
  • B. The Snow Queen
    The Snow Queen is an 1844 fairy tale by Hans Christian Andersen about a powerful, cold-hearted queen and the perilous journey of a young girl to rescue her friend from the queen’s icy realm.
  • C. The Snow Queen
    The Snow Queen is a novel by Michael Cunningham that intertwines the lives of two brothers in contemporary New York as they grapple with love, illness, and spiritual longing.
  • D. The Ugly Duckling
    "The Ugly Duckling" is a classic fairy tale by Hans Christian Andersen about an outcast young bird who grows into a beautiful swan, symbolizing personal transformation and the discovery of inner worth.
  • E. Gänseliesel
    Gänseliesel is a famous fountain statue in Göttingen, Germany, traditionally kissed by newly graduated students and considered one of the city’s most beloved landmarks.
  • 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_69d6ab4846e081908ee7bbd66a6d3459 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9043f82248190b05692aa0dc178a8 completed April 10, 2026, 2:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f6532a048190b53f96c9df948dda completed May 2, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:48 p.m.