Triple
T1637303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flight |
E35384
|
entity |
| Predicate | screenplayType |
P30807
|
FINISHED |
| Object | original screenplay |
—
|
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: original screenplay | Statement: [Flight, screenplayType, original screenplay]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: screenplayType Context triple: [Flight, screenplayType, original screenplay]
-
A.
screenplayBy
Indicates that a film, television show, or similar work was written or scripted by a particular person or group.
-
B.
filmType
Indicates the specific category or genre that a film belongs to.
-
C.
screenwriterOfWork
Indicates that a person served as the screenwriter (wrote the screenplay) for a particular creative work.
-
D.
bestOriginalScreenplayWinner
Indicates that the subject is the work (typically a film) that won the award for Best Original Screenplay in a given context or year.
-
E.
screenWriterAdaptationBy
Indicates that a person served as the screenwriter responsible for adapting an existing work into a screenplay.
- 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a96083e7308190abbf025fe8e43abb |
completed | March 5, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69a907cac610819083cafd4396b6d66c |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a9607716b4819092187f8c08daaf31 |
completed | March 5, 2026, 10:52 a.m. |
Created at: March 4, 2026, 7:28 p.m.