Triple
T6479683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Many Loves of Dobie Gillis |
E146156
|
entity |
| Predicate | televisionAdaptationGenre |
P57201
|
FINISHED |
| Object | situation comedy |
—
|
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: situation comedy | Statement: [The Many Loves of Dobie Gillis, televisionAdaptationGenre, situation comedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: televisionAdaptationGenre Context triple: [The Many Loves of Dobie Gillis, televisionAdaptationGenre, situation comedy]
-
A.
adaptationOfWorkGenre
chosen
Indicates that one work is an adaptation of another work that belongs to a specific genre.
-
B.
notableAdaptationType
Indicates that one work is a significant adaptation of another work in a specific way or medium (e.g., film adaptation, stage adaptation).
-
C.
televisionAdaptationStar
Indicates that a person is a starring actor in a television adaptation of a work.
-
D.
televisionFilm
Indicates that the subject is a television film (a movie produced for or originally distributed via television).
-
E.
isEpisodeOfGenre
Indicates that a specific episode belongs to or is categorized under a particular genre.
- 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_69c008fec7408190af7b146dc63d9750 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a4e764c819086828bb841f588e0 |
completed | March 22, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69c0673f6d48819080e10c85155c7195 |
completed | March 22, 2026, 10:03 p.m. |
Created at: March 22, 2026, 4:51 p.m.