Triple
T16514603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Indian princes |
E401148
|
entity |
| Predicate | oftenEducatedIn |
P7336
|
FINISHED |
| Object | British public schools |
—
|
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: British public schools | Statement: [Indian princes, oftenEducatedIn, British public schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenEducatedIn Context triple: [Indian princes, oftenEducatedIn, British public schools]
-
A.
educatedIn
chosen
Indicates that an entity received education or formal training at a specified institution or place.
-
B.
educatedAs
Indicates that an entity received education or training in a specific role, profession, or capacity.
-
C.
educatedAt
Indicates that an entity received education or formal training at a specified institution or place of learning.
-
D.
educates
Indicates that one entity provides instruction, knowledge, or training to another entity.
-
E.
hasEducationIn
Indicates that an entity has received education, training, or formal study in a specified field, subject, or discipline.
- 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_69d883838abc8190bc79cb2d41733ce2 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e7a5ff88190984e2f2bc2fd17cc |
completed | April 18, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69e296995d388190b88ebe189dce890d |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:14 a.m.