Triple
T36148675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Four Days Marches Foundation |
E1045522
|
entity |
| Predicate | eventFrequencyOfMainEvent |
P29070
|
FINISHED |
| Object | annual |
—
|
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 | Statement: [Four Days Marches Foundation, eventFrequencyOfMainEvent, annual]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eventFrequencyOfMainEvent Context triple: [Four Days Marches Foundation, eventFrequencyOfMainEvent, annual]
-
A.
timeBetweenMainEvents
Indicates the duration or interval separating two primary or key events in a sequence or process.
-
B.
numberOfMainEvents
Indicates the total count of primary or most significant events associated with a given entity or context.
-
C.
hasEventFrequency
chosen
Indicates how often a particular event occurs within a given time period.
-
D.
sportEventFrequency
Indicates how often a particular sport event occurs within a given time period.
-
E.
numberOfEvents
Indicates the quantity or count of events associated with a given entity or context.
- 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_69f76e37ace88190a906b107d388f5d1 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be53890081909b1d93f30a8f31c6 |
completed | May 3, 2026, 9:29 p.m. |
| PD | Predicate disambiguation | batch_69f7bccacbac8190978976324c67db28 |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:08 p.m.