Triple
T14699813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oxford High School |
E345264
|
entity |
| Predicate | hasNotableAlumnae |
P4387
|
FINISHED |
| Object | actresses |
—
|
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: actresses | Statement: [Oxford High School, hasNotableAlumnae, actresses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableAlumnae Context triple: [Oxford High School, hasNotableAlumnae, actresses]
-
A.
hasNotableFacultyAlumnus
Indicates that an individual is a distinguished former student who is recognized as notable faculty at a given institution.
-
B.
hasNotableAlumniInstitution
Indicates that an institution is associated with one or more notable alumni who previously attended or graduated from it.
-
C.
hasNotableAlumniType
chosen
Indicates that an entity has notable alumni belonging to a specified category or type.
-
D.
notableAlumnaOccupation
Indicates that the occupation specified is the professional role or career for a person who is a notable alumna of a particular institution.
-
E.
notableAlumnaRole
Indicates that a person, as a notable alumna, holds or has held a specific role or position associated with her alumni status.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb605f5948190ab6b20887c4b6833 |
completed | April 14, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:28 a.m.