Triple
T2179456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ERC Consolidator Grant |
E49005
|
entity |
| Predicate | fundingMode |
P59
|
FINISHED |
| Object | competitive |
—
|
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: competitive | Statement: [ERC Consolidator Grant, fundingMode, competitive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fundingMode Context triple: [ERC Consolidator Grant, fundingMode, competitive]
-
A.
fundingModel
chosen
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
-
B.
fundingSpeed
Indicates how quickly financial resources are provided or disbursed within a given funding relationship or process.
-
C.
settlementMode
Indicates the method or process by which a financial or transactional obligation is settled or completed between parties.
-
D.
funderType
Indicates the category or kind of organization or individual that provides funding in the relationship.
-
E.
fundedBy
Indicates that an entity receives financial support or resources from another entity.
- 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_69a88aa72d348190a9544bb5b8a4e71d |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc4358fc88190a6f556c2de9fef8c |
completed | March 7, 2026, 6:22 a.m. |
| PD | Predicate disambiguation | batch_69abbda0ec948190be88c1243d81a423 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:45 p.m.