Triple
T34047031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Finley as Winslow Leach |
E873111
|
entity |
| Predicate | disfigurementCauseInStory |
P178177
|
FINISHED |
| Object | recordPressAccident |
—
|
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: recordPressAccident | Statement: [William Finley as Winslow Leach, disfigurementCauseInStory, recordPressAccident]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disfigurementCauseInStory Context triple: [William Finley as Winslow Leach, disfigurementCauseInStory, recordPressAccident]
-
A.
causeInStory
Indicates that one event, action, or state functions as the cause of another within the narrative structure of a story.
-
B.
hasDisfiguredHero
Indicates that a work features a hero character who is physically disfigured.
-
C.
asterismCause
Indicates a causal relationship where one entity brings about or is responsible for the occurrence or formation of an asterism.
-
D.
causeOf
Indicates that one entity brings about, produces, or is responsible for the occurrence or existence of another entity or event.
-
E.
hasCauseNarrative
Indicates that one entity provides a narrative or explanatory account of the cause or causal background of another entity or event.
- 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_69f349a3ec2c8190b62da76e54231a0f |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f70c53062c8190b4cb7be22ab00bc7 |
completed | May 3, 2026, 8:50 a.m. |
| PD | Predicate disambiguation | batch_69f70ac0170c819098e3b8e41d02efef |
completed | May 3, 2026, 8:43 a.m. |
| PDg | Predicate description generation | batch_69f70b94784c8190970d654e066eb50d |
completed | May 3, 2026, 8:47 a.m. |
Created at: May 1, 2026, 1:51 a.m.