Triple

T4607970
Position Surface form Disambiguated ID Type / Status
Subject Clement Clarke Moore E100482 entity
Predicate hasSubject P450 FINISHED
Object Santa Claus E5879 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: Santa Claus | Statement: [Clement Clarke Moore, hasSubject, Santa Claus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Santa Claus
Context triple: [Clement Clarke Moore, hasSubject, Santa Claus]
  • A. Santa Claus chosen
    Santa Claus is a legendary, gift-giving figure in Western culture typically depicted as a jolly, bearded man in a red suit who delivers presents to children on Christmas Eve.
  • B. Rudolph
    Rudolph is the full given name of Rudy Giuliani, the former mayor of New York City and prominent American political figure.
  • C. Rudolph
    Rudolph is the legendary red-nosed reindeer from Christmas folklore who guides Santa Claus’s sleigh through the night.
  • D. San Nicolaas
    San Nicolaas is the second-largest city in Aruba, known for its oil refinery history, multicultural community, and vibrant street art scene.
  • E. Knecht Ruprecht
    Knecht Ruprecht is a traditional German folkloric figure who accompanies Saint Nicholas and is often depicted as a stern, punishing counterpart to reward-and-punishment Christmas customs.
  • 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_69bd43cce1e08190a07d53af6a9b6c24 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd599debdc81909d11d0e871c666bb completed March 20, 2026, 2:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa789cdc8190add02a5970f9a0b6 completed March 21, 2026, 1:55 a.m.
Created at: March 20, 2026, 1:12 p.m.