Triple
T24292025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gene Tierney as Laura Hunt |
E605848
|
entity |
| Predicate | diegeticStatusAtStart |
P61040
|
FINISHED |
| Object | presumed murdered |
—
|
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: presumed murdered | Statement: [Gene Tierney as Laura Hunt, diegeticStatusAtStart, presumed murdered]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: diegeticStatusAtStart Context triple: [Gene Tierney as Laura Hunt, diegeticStatusAtStart, presumed murdered]
-
A.
diegeticStatus
chosen
Indicates the relationship between an element and the narrative world, specifying whether it exists within the story’s reality (perceivable by characters) or outside it (only for the audience).
-
B.
statusAtStartOfFilm
Indicates the condition or situation an entity is in at the beginning of the film.
-
C.
legalStatusAtStartOfFilm
Indicates the legal condition or standing an entity has at the beginning of the film’s narrative.
-
D.
protagonistStatusAtStart
Indicates the role or condition the main character is in at the beginning of the narrative or event.
-
E.
hasDiegeticUse
Indicates that something is used or occurs within the narrative world itself, as experienced by the characters (i.e., it is diegetic).
- 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_69e29549335881909cbf27adcaba1cf0 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f29156ab8081909435b7178e9889bc |
completed | April 29, 2026, 11:16 p.m. |
| PD | Predicate disambiguation | batch_69f1c45c6ec081908401b69424428100 |
completed | April 29, 2026, 8:42 a.m. |
Created at: April 18, 2026, 12:09 a.m.