Triple
T490323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Constitutional Convention |
E9973
|
entity |
| Predicate | numberOfStatesRepresented |
P14077
|
FINISHED |
| Object | 12 |
—
|
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: 12 | Statement: [Constitutional Convention, numberOfStatesRepresented, 12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStatesRepresented Context triple: [Constitutional Convention, numberOfStatesRepresented, 12]
-
A.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
B.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or context.
-
C.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
D.
numberOfMemberStates
Indicates the total count of member states associated with a given entity or organization.
-
E.
numberOfStatesParties
Indicates the total count of entities that are formally parties to a given agreement, treaty, or arrangement.
- F. None of above. chosen
Provenance (4 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_69a2e802e2908190ab17c9479e0b6412 |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0e22a308190b04d12974fd08a38 |
completed | Feb. 28, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69a2edf63fbc819090ea6ca11f39116a |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebb2c908190960a4d0c014304cd |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.