Triple
T12196922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirk Gently (2010 TV series) |
E290611
|
entity |
| Predicate | hasHolisticDetectiveTheme |
P63938
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Dirk Gently (2010 TV series), hasHolisticDetectiveTheme, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHolisticDetectiveTheme Context triple: [Dirk Gently (2010 TV series), hasHolisticDetectiveTheme, true]
-
A.
hasClericalDetective
Indicates that an entity includes or is associated with a detective who is also a member of the clergy.
-
B.
hasFictionalDetective
chosen
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
C.
hasMotiveTheme
Indicates that an action, event, or situation is associated with a central motivating theme or underlying driving idea.
-
D.
hasPoliceTheme
Indicates that something features police, law enforcement, or policing activities as a central theme or focus.
-
E.
detectiveType
Indicates that one entity is classified as a particular type or category of detective in relation to another entity.
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d938cd2edc8190b1971349dbc0dee0 |
completed | April 10, 2026, 5:52 p.m. |
| PD | Predicate disambiguation | batch_69d91c38321c819080d500d0d64a04f6 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:50 p.m.