Triple
T20773691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prescription: Murder |
E511300
|
entity |
| Predicate | hasColumboTrait |
P133543
|
FINISHED |
| Object | inverted detective story structure |
—
|
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: inverted detective story structure | Statement: [Prescription: Murder, hasColumboTrait, inverted detective story structure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasColumboTrait Context triple: [Prescription: Murder, hasColumboTrait, inverted detective story structure]
-
A.
hasEnigmaticCharacter
Indicates that something possesses a mysterious, puzzling, or difficult-to-interpret quality or nature.
-
B.
hasNotableTraitInPlot
chosen
Indicates that a character or entity possesses a distinctive trait that plays a significant role within the narrative or plot.
-
C.
hasClericalDetective
Indicates that an entity includes or is associated with a detective who is also a member of the clergy.
-
D.
hasCrewCharacteristic
Indicates that a crew possesses a specified attribute, quality, or defining feature.
-
E.
hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
- 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_69e0b4ca01148190ac018e57e0cab46f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c269638881909d96b847f7de5585 |
completed | April 21, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69e5c0550ec481908a0877fb2409d983 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:37 p.m.