Triple
T37581835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Saint prend l’affût |
E934988
|
entity |
| Predicate | featuresFictionalGentlemanThief |
P61174
|
FINISHED |
| Object | Simon Templar |
E467659
|
NE 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: Simon Templar | Statement: [Le Saint prend l’affût, featuresFictionalGentlemanThief, Simon Templar]
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 (4 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_69f76ece61dc8190a0ab33f8d87d0a7e |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40cda96a6081909689503810bc81a7 |
completed | June 28, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_6a037a1553e08190bb7424c448cb1f33 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:17 p.m.