Triple
T37581834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Saint prend l’affût |
E934988
|
entity |
| Predicate | hasMainProtagonistAlias |
P46224
|
FINISHED |
| Object | The Saint |
E378032
|
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: The Saint | Statement: [Le Saint prend l’affût, hasMainProtagonistAlias, The Saint]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainProtagonistAlias Context triple: [Le Saint prend l’affût, hasMainProtagonistAlias, The Saint]
-
A.
protagonistAlsoKnownAs
chosen
Indicates that an entity serving as a protagonist is alternatively referred to by another name or alias.
-
B.
hasFictionalAlias
Indicates that an entity is known by an alternative name or identity within a fictional context.
-
C.
hasFictionalAlterEgoOf
Indicates that one entity is the fictional alter ego, persona, or alternate identity of another entity.
-
D.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
E.
hasFictionalTitleOfProtagonist
Indicates that an entity holds or is assigned the fictional title or role used to designate the main protagonist in a narrative.
- 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_6a40afdb36c88190b0d5ee0d61ac6459 |
completed | June 28, 2026, 5:23 a.m. |
| PD | Predicate disambiguation | batch_6a037a1553e08190bb7424c448cb1f33 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:17 p.m.