Triple
T11186862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elijah Shaw |
E264691
|
entity |
| Predicate | typeOfBenefaction |
P73174
|
FINISHED |
| Object | support for 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: support for higher education | Statement: [Elijah Shaw, typeOfBenefaction, support for higher education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfBenefaction Context triple: [Elijah Shaw, typeOfBenefaction, support for higher education]
-
A.
typeOfPatronage
chosen
Indicates the specific kind or category of support, sponsorship, or backing that one entity provides to another.
-
B.
philanthropicBeneficiary
Indicates that one entity is the recipient or target of another entity’s philanthropic giving or charitable support.
-
C.
sectorBenefited
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or entity.
-
D.
benefice
Indicates that one entity grants or bestows a benefit, favor, or advantage upon another.
-
E.
benefitsCause
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or cause.
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8abbeac8190ad6e419258999f4e |
completed | April 9, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69d75cf4461c8190af84060f7db83211 |
completed | April 9, 2026, 8:01 a.m. |
Created at: April 8, 2026, 9:29 p.m.