Triple
T572775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trinity College, Cambridge |
E13697
|
entity |
| Predicate | hasNobelLaureatesAmongAlumni |
P324
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Trinity College, Cambridge, hasNobelLaureatesAmongAlumni, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNobelLaureatesAmongAlumni Context triple: [Trinity College, Cambridge, hasNobelLaureatesAmongAlumni, yes]
-
A.
hasNobelLaureatesAffiliated
chosen
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.
hasNotableAlumniType
Indicates that an entity has notable alumni belonging to a specified category or type.
-
D.
hasHonoraryDegreeFrom
Indicates that an individual has been awarded an honorary degree by a particular institution.
-
E.
typicalNumberOfLaureatesPerYear
Indicates the usual or average number of laureates associated with a given award or context in a single year.
- 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_69a4933fa4d88190a7949cc83c08c5c1 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b49bad88190bc73d31a317c0ef4 |
completed | March 1, 2026, 8:02 p.m. |
| PD | Predicate disambiguation | batch_69a494c4969c819080375d08f9eec50c |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.