Triple
T28585081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Stanwyck as Lee Leander |
E723474
|
entity |
| Predicate | filmRuntimeApproximate |
P36872
|
FINISHED |
| Object | about 94 minutes |
—
|
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: about 94 minutes | Statement: [Barbara Stanwyck as Lee Leander, filmRuntimeApproximate, about 94 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmRuntimeApproximate Context triple: [Barbara Stanwyck as Lee Leander, filmRuntimeApproximate, about 94 minutes]
-
A.
filmRuntimeApprox
chosen
Indicates an approximate or estimated duration of a film, rather than its exact runtime.
-
B.
filmRuntimeMinutes
Indicates the duration of a film expressed in minutes.
-
C.
filmLength
Indicates the duration or running time of a film, typically measured in units such as minutes.
-
D.
filmLengthSpecialization
Indicates a relationship where one entity specifies or refines the particular length or duration characteristics of a film defined by another entity.
-
E.
featureLengthFilm
Indicates that the subject is a film whose running time meets or exceeds the standard length considered to be a feature film.
- 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_69f01d7f92e481909847f5f3f3174a89 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f650cef6c88190b119b2d0ea9ea4cf |
completed | May 2, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69f64cb0d8008190912e1430cfaf92aa |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 4:17 a.m.