Triple
T5706311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winter's Tale (2014 film) |
E125790
|
entity |
| Predicate | hasMythicSetting |
P65692
|
FINISHED |
| Object | mythic New York City |
—
|
LITERAL 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: mythic New York City | Statement: [Winter's Tale (2014 film), hasMythicSetting, mythic New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMythicSetting Context triple: [Winter's Tale (2014 film), hasMythicSetting, mythic New York City]
-
A.
hasMythicMotif
Indicates that one entity features, embodies, or is associated with a particular mythic motif found in the other entity.
-
B.
hasMythicTheme
Indicates that something embodies, references, or is characterized by a mythic or mythological theme.
-
C.
hasMythicFunction
Indicates that something serves a symbolic, narrative, or ritual role within a mythic or mythological framework.
-
D.
hasMythType
Indicates that an entity is associated with or classified under a particular type or category of myth.
-
E.
hasMythicEvent
Indicates that an entity is associated with, involves, or features a significant mythic or legendary event.
- F. None of above. chosen
Provenance (4 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_69c0082d6fe48190b777fb383769e5c8 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0248751bc8190b12aaa42d1ef17e3 |
completed | March 22, 2026, 5:19 p.m. |
| PD | Predicate disambiguation | batch_69c021c2d8bc8190b947c7d1f423d2f3 |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c023dfec6881909ee6189b874b4348 |
completed | March 22, 2026, 5:16 p.m. |
Created at: March 22, 2026, 3:45 p.m.