Triple
T2174467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ERC Starting Grant |
E48494
|
entity |
| Predicate | applicationFrequency |
P16914
|
FINISHED |
| Object | annual calls |
—
|
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: annual calls | Statement: [ERC Starting Grant, applicationFrequency, annual calls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: applicationFrequency Context triple: [ERC Starting Grant, applicationFrequency, annual calls]
-
A.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
B.
performedFrequency
chosen
Indicates how often an action or activity is carried out within a given time period.
-
C.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
-
D.
laterFrequency
Indicates that one event, state, or action occurs with a lower frequency than another in a temporal sequence.
-
E.
meetingFrequency
Indicates how often a meeting or recurring gathering takes place over a given period.
- 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_69a88aa3faa48190995b233af6525815 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc1559ff481908efe3f214b2570dc |
completed | March 7, 2026, 6:10 a.m. |
| PD | Predicate disambiguation | batch_69abbd9efc1c81909a65044a1ffc9038 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:45 p.m.