Triple
T20351715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uttar Falguni |
E496026
|
entity |
| Predicate | filmRuntimeStatus |
P74363
|
FINISHED |
| Object | feature 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: feature film | Statement: [Uttar Falguni, filmRuntimeStatus, feature film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmRuntimeStatus Context triple: [Uttar Falguni, filmRuntimeStatus, feature film]
-
A.
filmStatus
Indicates the current production, release, or availability state of a film (e.g., announced, in production, released, cancelled).
-
B.
filmRuntimeApprox
Indicates an approximate or estimated duration of a film, rather than its exact runtime.
-
C.
statusDuringFilm
Indicates that a particular status or condition holds for an entity during the time span in which a specified film takes place or is being made.
-
D.
filmRuntimeMinutes
Indicates the duration of a film expressed in minutes.
-
E.
hasRunningTimeCategory
chosen
Indicates that an entity is associated with a specific category based on its running time or duration.
- 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_69e0b4a3f7f48190b37f354574028ca6 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67850ace48190b19aff5780fef7e8 |
completed | April 20, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69e57636b4808190bc2855af48a3ccdc |
completed | April 20, 2026, 12:41 a.m. |
Created at: April 16, 2026, 11:24 a.m.