Triple
T14106778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Docteur Pascal |
E339525
|
entity |
| Predicate | hasCentralProfessionOfProtagonist |
P21567
|
FINISHED |
| Object | doctor |
—
|
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: doctor | Statement: [Le Docteur Pascal, hasCentralProfessionOfProtagonist, doctor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCentralProfessionOfProtagonist Context triple: [Le Docteur Pascal, hasCentralProfessionOfProtagonist, doctor]
-
A.
hasProfessionTrait
Indicates that an entity possesses a particular characteristic, quality, or attribute specifically related to their profession or occupational role.
-
B.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
C.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
D.
hasClericalProtagonist
Indicates that the main character in the work is a member of the clergy or holds a religious office.
-
E.
hasInterpreterProfession
Indicates that an entity works in the professional role or occupation of an interpreter.
- 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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de600ada808190b92d67dc30f13d15 |
completed | April 14, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69de05b2f7e481908a9a7d40153234c0 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:22 p.m.