Triple
T105222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Italian Market |
E2123
|
entity |
| Predicate | sceneType |
P4566
|
FINISHED |
| Object | training montage location in Rocky |
—
|
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: training montage location in Rocky | Statement: [Italian Market, sceneType, training montage location in Rocky]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sceneType Context triple: [Italian Market, sceneType, training montage location in Rocky]
-
A.
landscapeStyle
Indicates the design style or aesthetic approach applied to a landscape or outdoor environment.
-
B.
surfaceType
Indicates the kind or classification of surface associated with an entity or interaction.
-
C.
typicalBackground
chosen
Indicates that an entity has a usual or commonly expected background, context, or setting associated with it.
-
D.
sessionType
Indicates the classification or category of a particular session based on its purpose, format, or context.
-
E.
trackType
Indicates the specific kind or category of track associated with an entity, such as its functional or physical classification.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25711f6788190a22252ea3a3af394 |
completed | Feb. 28, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69a2563be81c81908ccc5ed44edd6b8e |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.