Triple
T26610424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gerard Gordeau |
E667902
|
entity |
| Predicate | trainerRole |
P41095
|
FINISHED |
| Object | coach at his own dojo in the Netherlands |
—
|
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: coach at his own dojo in the Netherlands | Statement: [Gerard Gordeau, trainerRole, coach at his own dojo in the Netherlands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainerRole Context triple: [Gerard Gordeau, trainerRole, coach at his own dojo in the Netherlands]
-
A.
trainer
chosen
Indicates a relationship where one entity teaches, coaches, or prepares another entity to develop skills, knowledge, or performance in a particular domain.
-
B.
roleInTrain
Indicates the specific function or position an entity holds within the context of a train (e.g., passenger, conductor, locomotive, or car type).
-
C.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
D.
coachedRole
Indicates that one entity served as a coach for another entity in a specific role or position.
-
E.
pretrainingRole
Indicates the role or function an entity serves specifically during a pretraining phase or process.
- 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_69ee9cfd20348190bb1255d2603efb7a |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f615a87a5881908a96dcc673adc3b5 |
completed | May 2, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69f602d7b1b0819095ddd3b5169f8ce2 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 27, 2026, 2:16 a.m.