Triple
T35709382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autour d’une cabine |
E1031809
|
entity |
| Predicate | eraOfAnimation |
P185359
|
FINISHED |
| Object | pre-cinematic animation |
—
|
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: pre-cinematic animation | Statement: [Autour d’une cabine, eraOfAnimation, pre-cinematic animation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eraOfAnimation Context triple: [Autour d’une cabine, eraOfAnimation, pre-cinematic animation]
-
A.
eraOfStory
Indicates the historical or temporal period in which the events of a story take place.
-
B.
eraOfFigure
Indicates the historical period or era during which a particular figure was active, influential, or primarily associated.
-
C.
eraOfSetting
Indicates the historical or temporal period in which the setting of something (such as a story, event, or work) takes place.
-
D.
eraType
Indicates the classification of a time period or era according to its type or category.
-
E.
eraOfCollection
Indicates the historical time period during which the collection was assembled or gathered.
- 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_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be520f148190ba200bf3dbf40656 |
completed | May 3, 2026, 9:29 p.m. |
Created at: May 3, 2026, 4:05 p.m.