Triple
T27319301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonin Besse |
E689441
|
entity |
| Predicate | hasBenefactionIn |
P143234
|
FINISHED |
| Object | higher education |
—
|
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: higher education | Statement: [Antonin Besse, hasBenefactionIn, higher education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBenefactionIn Context triple: [Antonin Besse, hasBenefactionIn, higher education]
-
A.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
B.
hasNotableBenefaction
chosen
Indicates that an entity has provided a significant or noteworthy benefit, donation, or contribution to another entity or cause.
-
C.
hasBenefitType
Indicates that an entity is associated with a specific category or type of benefit it provides or receives.
-
D.
belongsToBenefice
Indicates that something is under the ecclesiastical ownership, authority, or endowment of a particular benefice.
-
E.
benefice
Indicates that one entity grants or bestows a benefit, favor, or advantage upon another.
- 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_69ef355c53a08190a8a92e355a7ce115 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f68805b4848190b75da14996d52a38 |
completed | May 2, 2026, 11:25 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 27, 2026, 11:32 a.m.