Triple
T1490405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | W3C Fellow |
E29563
|
entity |
| Predicate | typicalEmployer |
P28583
|
FINISHED |
| Object | research institution |
—
|
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: research institution | Statement: [W3C Fellow, typicalEmployer, research institution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEmployer Context triple: [W3C Fellow, typicalEmployer, research institution]
-
A.
typicalEmployerUnit
Indicates that one entity is the standard or characteristic organizational unit that employs or is expected to employ another entity.
-
B.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
C.
collegeEmployer
Indicates that a college or university is the employing institution of a given person or organization.
-
D.
notableEmployer
Indicates that an entity has been employed by, or has worked for, a particularly significant or noteworthy organization or individual.
-
E.
employersInclude
Indicates that a specified set or group of employers contains, as members, the employer or employers referenced by the other argument.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6c233ec819087e1233af02aabfc |
completed | March 1, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69a4c48902808190a8028d359bcf123e |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c52c703c8190a56389b09d97659f |
completed | March 1, 2026, 11:01 p.m. |
Created at: March 1, 2026, 8:12 p.m.