Triple
T7644422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Do You Want to Build a Snowman? |
E173084
|
entity |
| Predicate | chronologicalPositionInFilm |
P10630
|
FINISHED |
| Object | early in the film |
—
|
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: early in the film | Statement: [Do You Want to Build a Snowman?, chronologicalPositionInFilm, early in the film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chronologicalPositionInFilm Context triple: [Do You Want to Build a Snowman?, chronologicalPositionInFilm, early in the film]
-
A.
chronologicalPosition
chosen
Indicates the relative ordering of one event or entity in time with respect to another.
-
B.
chronologicalPositionInLabors
Indicates the specific order or sequence in which a given labor occurs within a defined series of labors.
-
C.
positionInStory
Indicates the point or role an event, character, or element occupies within the overall sequence or structure of a story.
-
D.
chronologicalOrderInSeries
Indicates that one entity appears earlier or later than another within an ordered sequence or series.
-
E.
positionInCast
Indicates the specific role or placement an individual holds within the cast of a production, such as their billing order or credited position.
- F. None of above.
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_69c6995360188190968ee57b72a1627f |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6faf13858819095262664e1e04eb7 |
completed | March 27, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e8cadc8190b7977fcd213954dd |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:58 p.m.