Triple
T35709372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autour d’une cabine |
E1031809
|
entity |
| Predicate | frameSource |
P184442
|
FINISHED |
| Object | long flexible film band with sequential images |
—
|
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: long flexible film band with sequential images | Statement: [Autour d’une cabine, frameSource, long flexible film band with sequential images]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frameSource Context triple: [Autour d’une cabine, frameSource, long flexible film band with sequential images]
-
A.
frameSupplier
Indicates that one entity supplies or provides frames (such as structural or supporting frameworks) to another entity.
-
B.
frame
Indicates placing or presenting something within a particular context, structure, or perspective that shapes how it is interpreted.
-
C.
framesAs
Indicates how one entity presents, characterizes, or interprets another entity or situation in a particular light or context.
-
D.
framesViewOf
Indicates that one entity provides a framing, perspective, or interpretive context through which another entity is viewed or understood.
-
E.
frameDevice
Indicates that one entity serves as a structural or supporting frame for another device or object.
- 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_69f76e0df1d08190965b1c6dff94c391 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69f7b0e3917481908a394680d76743c3 |
completed | May 3, 2026, 8:32 p.m. |
Created at: May 3, 2026, 4:05 p.m.