Triple
T29940731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chilean universities |
E760488
|
entity |
| Predicate | offerFieldOfStudy |
P2582
|
FINISHED |
| Object | engineering |
—
|
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: engineering | Statement: [Chilean universities, offerFieldOfStudy, engineering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offerFieldOfStudy Context triple: [Chilean universities, offerFieldOfStudy, engineering]
-
A.
offersFieldOfStudy
chosen
Indicates that an institution or program provides a particular field of study as an available area of academic focus.
-
B.
offersLevelOfStudy
Indicates that an institution or program provides a particular level or type of study (e.g., undergraduate, graduate, doctoral) as an available option.
-
C.
characterFieldOfStudy
Indicates the academic or disciplinary field that a character studies or specializes in.
-
D.
offersFieldEducation
Indicates that one entity provides practical, field-based educational or training opportunities to another.
-
E.
hasModeOfStudy
Indicates the method or format by which an entity (typically a learner) undertakes their studies, such as full-time, part-time, online, or in-person.
- 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_69f22463f3648190a603c3ff305c660b |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fcc4b700748190ae00b21d09c96695 |
completed | May 7, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69fcb0f9d3d881908a049475182fb039 |
completed | May 7, 2026, 3:34 p.m. |
Created at: April 29, 2026, 6:22 p.m.