Triple
T14226287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auntie Mame (screenplay adaptation) |
E352623
|
entity |
| Predicate | timeSpanCoveredInStory |
P10692
|
FINISHED |
| Object | several decades |
—
|
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: several decades | Statement: [Auntie Mame (screenplay adaptation), timeSpanCoveredInStory, several decades]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeSpanCoveredInStory Context triple: [Auntie Mame (screenplay adaptation), timeSpanCoveredInStory, several decades]
-
A.
narrativeTimeSpanHours
Indicates the duration of a narrative or story event measured in hours.
-
B.
timeOfNarrative
Indicates the specific time or period during which the events of a narrative are set or unfold.
-
C.
storyTimeSpanInFilm
chosen
Indicates the duration of time that the story or narrative covers within the film.
-
D.
timeHorizonCovered
Indicates the span or duration of time that is encompassed or addressed by something (such as a plan, forecast, or agreement).
-
E.
timePeriodCoveredTo
Indicates the span or duration of time that is encompassed, addressed, or relevant to a given subject or entity.
- 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_69d8278a06e481908b5d6af0a8afe737 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6228e53c8190abbe4e2d88a7362a |
completed | April 14, 2026, 3:50 p.m. |
| PD | Predicate disambiguation | batch_69de05bf069c8190b69f00f00f5eb126 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:06 a.m.