Triple
T44942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rita R. Colwell |
E882
|
entity |
| Predicate | hasHonoraryDegreeFrom |
P2837
|
FINISHED |
| Object | numerous universities worldwide |
—
|
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: numerous universities worldwide | Statement: [Rita R. Colwell, hasHonoraryDegreeFrom, numerous universities worldwide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHonoraryDegreeFrom Context triple: [Rita R. Colwell, hasHonoraryDegreeFrom, numerous universities worldwide]
-
A.
hasNobelLaureatesAffiliated
Indicates that one entity has Nobel Prize laureates formally associated or connected with it (e.g., as members, staff, or alumni).
-
B.
hasLaureate
Indicates that an entity (such as an award or prize) has a specific person or group as its laureate or recipient.
-
C.
hasViceChancellor
Indicates that one entity serves as the vice chancellor of another entity.
-
D.
isOrderOfKnighthood
Indicates that an entity is a formal chivalric or knightly order to which individuals can be admitted.
-
E.
hasAcademicAffiliation
Indicates that an entity is formally associated with an academic institution, such as through employment, enrollment, or official collaboration.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24ba7016481909d595402712db6e2 |
completed | Feb. 28, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_69a24abbd32c81908cec461d9097662e |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24ba5da048190a484963cb5a9bb2b |
completed | Feb. 28, 2026, 1:57 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.