Triple
T34893633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colonel Saito |
E1006367
|
entity |
| Predicate | laterPopularizedIn |
P2352
|
FINISHED |
| Object | feature film adaptation |
—
|
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: feature film adaptation | Statement: [Colonel Saito, laterPopularizedIn, feature film adaptation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterPopularizedIn Context triple: [Colonel Saito, laterPopularizedIn, feature film adaptation]
-
A.
popularizedIn
chosen
Indicates that something became widely known, accepted, or fashionable within a particular place, time period, or context.
-
B.
popularizedOn
Indicates that something became widely known, accepted, or fashionable through a particular platform, medium, or context.
-
C.
popularizedBy
Indicates that something became widely known, accepted, or fashionable as a result of the influence or actions of a particular agent.
-
D.
popularizedAfter
Indicates that one entity became widely known, accepted, or influential only after another specified entity had already gained popularity.
-
E.
popularizedSince
Indicates that one entity has become widely known, accepted, or influential starting from a specified time or period.
- 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_69f76dbfe5788190ad8b64f241f470c8 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a0048697bd081909073640666ca6a96 |
completed | May 10, 2026, 8:57 a.m. |
| PD | Predicate disambiguation | batch_6a0047bf3c248190a9ac97a7afdfe2cb |
completed | May 10, 2026, 8:54 a.m. |
Created at: May 3, 2026, 4 p.m.