Triple
T14672219
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A.P. Møller and Chastine Mc-Kinney Møller Foundation |
E344540
|
entity |
| Predicate | hasTypeOfGrant |
P10368
|
FINISHED |
| Object | project grants |
—
|
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: project grants | Statement: [A.P. Møller and Chastine Mc-Kinney Møller Foundation, hasTypeOfGrant, project grants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfGrant Context triple: [A.P. Møller and Chastine Mc-Kinney Møller Foundation, hasTypeOfGrant, project grants]
-
A.
hasGrantmakingType
Indicates the specific category or mode of grantmaking associated with an entity, such as how it provides or administers grants.
-
B.
typeOfGrant
chosen
Indicates the specific category or kind of grant associated with an entity.
-
C.
typeOfGrantManaged
Indicates that one entity manages or administers a specific type or category of grant in relation to another entity or context.
-
D.
granteeType
Indicates the classification or category of the entity that receives a grant or is granted a right, benefit, or permission.
-
E.
hasGradeType
Indicates that an entity is associated with a particular category or type of grade (e.g., letter grade, pass/fail, percentage).
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb55064cc8190b9669d0b2da61825 |
completed | April 14, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69de6576f0208190aa94d995e797ac38 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:27 a.m.