Triple
T1509213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Notes on the State of Virginia |
E33974
|
entity |
| Predicate | numberOfQueries |
P29380
|
FINISHED |
| Object | 23 |
—
|
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: 23 | Statement: [Notes on the State of Virginia, numberOfQueries, 23]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfQueries Context triple: [Notes on the State of Virginia, numberOfQueries, 23]
-
A.
numberOfQuestions
Indicates the total count of questions associated with or contained in a given entity or context.
-
B.
numberOfTests
Indicates the quantity of tests associated with or performed in a given context or entity.
-
C.
numberOfTargets
Indicates the quantity of target entities associated with or affected by a given subject or event.
-
D.
numberOfMembersReturned
Indicates the quantity of members that are provided or yielded as a result of an operation or query.
-
E.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
- 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_69a885f352a4819099b24ff15489dede |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a8e2dd93dc8190a78443900e8d5564 |
completed | March 5, 2026, 1:56 a.m. |
| PD | Predicate disambiguation | batch_69a88728c150819095cdcdbfcabf4249 |
completed | March 4, 2026, 7:25 p.m. |
| PDg | Predicate description generation | batch_69a8e2dbc848819084eb0d50189fa967 |
completed | March 5, 2026, 1:56 a.m. |
Created at: March 4, 2026, 7:24 p.m.