Triple
T15237185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jake from State Farm |
E364157
|
entity |
| Predicate | settingOfFamousScene |
P86909
|
FINISHED |
| Object | late-night phone call |
—
|
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: late-night phone call | Statement: [Jake from State Farm, settingOfFamousScene, late-night phone call]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfFamousScene Context triple: [Jake from State Farm, settingOfFamousScene, late-night phone call]
-
A.
notableScene
Indicates that a particular scene is especially significant, memorable, or noteworthy within a work or context.
-
B.
filmSceneType
chosen
Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
-
C.
placeOfShooting
Indicates the location where a shooting event took place.
-
D.
formerFilmingLocation
Indicates that a place was once used as a filming location for a work but is no longer used for that purpose.
-
E.
filmingLocationContext
Indicates the contextual relationship specifying where the filming of an event, scene, or production took place.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007da7e988190925a9b67b8070bc7 |
completed | April 15, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69deca899d5c8190be4a7c71e1683c69 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:12 a.m.