Triple
T34821047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Morel |
E1003774
|
entity |
| Predicate | educationAndCareer |
P55489
|
FINISHED |
| Object | wins a good position in London |
—
|
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: wins a good position in London | Statement: [William Morel, educationAndCareer, wins a good position in London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationAndCareer Context triple: [William Morel, educationAndCareer, wins a good position in London]
-
A.
studCareer
chosen
Indicates that a student is pursuing or associated with a particular academic or professional career path.
-
B.
educationAspiration
Indicates an individual's intended or desired level of education or educational achievement.
-
C.
collegeCareer
Indicates the period of a person’s academic and related activities while they are enrolled in and progressing through college.
-
D.
studCareerAt
Indicates that a student pursued or developed their academic or professional career at a particular institution or organization.
-
E.
educationField
Indicates the academic or professional discipline in which an entity has been educated or trained.
- 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_69f76db717088190811b4e744610f37d |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77addb72c81909331e94d2f0f6b62 |
completed | May 3, 2026, 4:42 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 4 p.m.