Triple
T2417279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | École Centrale des Arts et Manufactures |
E52333
|
entity |
| Predicate | targetCareer |
P37798
|
FINISHED |
| Object | industry leadership positions |
—
|
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: industry leadership positions | Statement: [École Centrale des Arts et Manufactures, targetCareer, industry leadership positions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetCareer Context triple: [École Centrale des Arts et Manufactures, targetCareer, industry leadership positions]
-
A.
careerField
Indicates the professional domain or occupational area in which an entity works or specializes.
-
B.
plannedCareer
Indicates that an individual has chosen and intends to pursue a specific career path.
-
C.
careerAssists
Indicates the total number of assists a player has recorded over the entire span of their professional or competitive career.
-
D.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
E.
careerPath
Indicates the progression or sequence of roles, positions, or occupations that an individual follows over time in their professional life.
- 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_69ab495622948190bc6bc6e4cddaf645 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc94eafd481909eeff689e5bf5960 |
completed | March 7, 2026, 6:44 a.m. |
| PD | Predicate disambiguation | batch_69abc5a6cbd0819086c0716e266b7ebb |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc6011e348190b6f9c038c7559289 |
completed | March 7, 2026, 6:30 a.m. |
Created at: March 6, 2026, 9:42 p.m.