Triple
T35708468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L’Homme foudroyé |
E1031783
|
entity |
| Predicate | hasAuthorAsCharacter |
P194037
|
FINISHED |
| Object | Blaise Cendrars |
—
|
NE NERFINISHED |
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: Blaise Cendrars | Statement: [L’Homme foudroyé, hasAuthorAsCharacter, Blaise Cendrars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorAsCharacter Context triple: [L’Homme foudroyé, hasAuthorAsCharacter, Blaise Cendrars]
-
A.
hasAuthorCharacteristic
Indicates that an author possesses a particular attribute, trait, or quality.
-
B.
leadActorPlaysAuthorCharacter
Indicates that the film’s lead actor portrays a character who is an author.
-
C.
hasAuthorOf
Indicates that one entity is the author or creator of another entity (such as a work, document, or publication).
-
D.
hasAuthor
Indicates that an entity is written or created by a specific author.
-
E.
writtenForCharacter
Indicates that a piece of writing (such as a script, scene, or dialogue) was specifically created or tailored for a particular character.
- F. None of above. chosen
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_69f76e0d393c8190b6303c64408736db |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd5d48855c8190bd93070b6a00d8b5 |
completed | May 8, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69fd5c9aabb88190912800d90184a89d |
completed | May 8, 2026, 3:46 a.m. |
| PDg | Predicate description generation | batch_69fd5d47da488190a4f2dbd44a0a83b2 |
completed | May 8, 2026, 3:49 a.m. |
Created at: May 3, 2026, 4:05 p.m.