Triple
T37581835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Saint prend l’affût |
E934988
|
entity |
| Predicate | featuresFictionalGentlemanThief |
—
|
GENERATED |
| Object | Simon Templar |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresFictionalGentlemanThief Context triple: [Le Saint prend l’affût, featuresFictionalGentlemanThief, Simon Templar]
-
A.
featuresFictionalMurdererType
Indicates that the subject includes or portrays a specific type or category of fictional murderer.
-
B.
hasThiefCharacter
chosen
Indicates that an entity includes or features a character whose role or identity is that of a thief.
-
C.
hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
D.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
E.
featuresPrivateDetective
Indicates that the subject includes or involves a private detective as a notable element or character.
- F. None of above.
Provenance (1 batch)
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_69f76ece61dc8190a0ab33f8d87d0a7e |
completed | May 3, 2026, 3:50 p.m. |
Created at: May 3, 2026, 4:17 p.m.