Triple
T13215401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Co-operative Commonwealth Federation |
E314599
|
entity |
| Predicate | peakFederalSeatCount |
P4273
|
FINISHED |
| Object | 28 |
—
|
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: 28 | Statement: [Co-operative Commonwealth Federation, peakFederalSeatCount, 28]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakFederalSeatCount Context triple: [Co-operative Commonwealth Federation, peakFederalSeatCount, 28]
-
A.
numberOfRepresentatives
chosen
Indicates the quantity of representatives associated with a given entity or unit.
-
B.
numberOfStatesRepresented
Indicates how many distinct states are represented or covered in a given context or entity.
-
C.
numberOfSenateDistricts
Indicates the total count of senate districts associated with a given entity or jurisdiction.
-
D.
federalSubjectCount
Indicates the number of federal subjects (administrative units within a federation) associated with or contained by an entity.
-
E.
numberOfSeatsInSenate
Indicates the total count of seats allocated in a given senate.
- 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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf28c9c819080d7b42d20f579d1 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc938f081909f123bdf1263ff7f |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:18 p.m.