Triple
T37265380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Speedy Claxton |
E924366
|
entity |
| Predicate | isAlumnusOf |
P86301
|
FINISHED |
| Object | Hofstra University |
—
|
NE NERFINISHED |
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: Hofstra University | Statement: [Speedy Claxton, isAlumnusOf, Hofstra University]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAlumnusOf Context triple: [Speedy Claxton, isAlumnusOf, Hofstra University]
-
A.
usesAlmaMater
Indicates that one entity makes use of, relies on, or leverages the educational institution where another entity studied or graduated.
-
B.
hasAlumni
Indicates that an institution or organization is associated with individuals who formerly attended or graduated from it.
-
C.
namedAfterAlumnusOf
Indicates that one entity is named in honor of a person who is an alumnus of another specified institution or organization.
-
D.
servesAsAlmaMaterOf
chosen
Indicates that an educational institution is the alma mater of a person or entity, meaning they previously studied or graduated there.
-
E.
fieldAssociatedWithAlumnus
Indicates that a particular field of study, profession, or area of expertise is associated with or pursued by a given alumnus.
- 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_69f76eabd6c481909d414a80a1345c98 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb4134225081909fd60703b8cae397 |
completed | May 6, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69fb35bf767081908de8345358ca7f44 |
completed | May 6, 2026, 12:36 p.m. |
Created at: May 3, 2026, 4:15 p.m.