Triple
T19413331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Chocolate Shop |
E485644
|
entity |
| Predicate | usesMotif |
P2366
|
FINISHED |
| Object | Santa Claus |
—
|
NE NERFINISHED |
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: [Santa Chocolate Shop, usesMotif, Santa Claus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Claus Context triple: [Santa Chocolate Shop, usesMotif, 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.
Santa
Santa is another name for the Dongxiang language, a Mongolic language spoken primarily by the Dongxiang ethnic group in Gansu Province, China.
-
C.
Santa
Santa is a small city in Egypt’s Gharbia Governorate, located in the Nile Delta region and known primarily as a local agricultural and market center.
-
D.
Santa
Santa is a character in Cesare Pavese’s novel "La luna e i falò," representing the rural, often harsh realities of life in the Piedmont countryside.
-
E.
St. Niklaus
St. Niklaus is a locality within the Swiss municipality of Feldbrunnen-St. Niklaus in the canton of Solothurn.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62af77eb481909fcfb6cdde1e8580 |
completed | April 20, 2026, 1:32 p.m. |
Created at: April 10, 2026, 1:37 p.m.