Triple
T3728732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snow (novel) |
E79010
|
entity |
| Predicate | originalTitle |
P65
|
FINISHED |
| Object | Kar |
E240310
|
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: Kar | Statement: [Snow (novel), originalTitle, Kar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kar Context triple: [Snow (novel), originalTitle, Kar]
-
A.
Kar
chosen
Kar is the young, streetwise pickpocket chosen as the reluctant successor to a mystical protector in the action film "Bulletproof Monk."
-
B.
Ka
Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
-
C.
Kat
Kat is a given name, typically used as a shortened or informal form of Kathleen or Katherine.
-
D.
Ke
Ke is the given name of Ke Huy Quan, the Vietnamese-American actor and former child star known for roles in films like "Indiana Jones and the Temple of Doom," "The Goonies," and "Everything Everywhere All at Once."
-
E.
Kir
Kir is a classic French apéritif cocktail traditionally made by mixing dry white wine with crème de cassis (blackcurrant liqueur).
- 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_69ad8b0e4650819090ad7cef094285e8 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb17432881909390284b935ed3fd |
completed | March 8, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db13d34881909fa74c682184b797 |
completed | March 14, 2026, 3:50 a.m. |
Created at: March 8, 2026, 3:34 p.m.