Triple
T14808329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Insuricare |
E348100
|
entity |
| Predicate | settingForSceneType |
P1957
|
FINISHED |
| Object | office scenes |
—
|
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: office scenes | Statement: [Insuricare, settingForSceneType, office scenes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingForSceneType Context triple: [Insuricare, settingForSceneType, office scenes]
-
A.
performedInSceneType
Indicates that an action or event was carried out within a scene of a specified type or category.
-
B.
filmSceneType
Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
-
C.
setting
chosen
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
D.
scenarioType
Indicates the specific category or kind of situation, context, or use case that an entity or event is associated with.
-
E.
scenographyStyle
Indicates the stylistic approach or design aesthetic used in the staging and visual arrangement of a scene or performance.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decf33b6a08190ab6a4cfeda2cc09c |
completed | April 14, 2026, 11:35 p.m. |
| PD | Predicate disambiguation | batch_69de8c0ef8a4819092d84478b1f56db1 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:44 a.m.