Triple
T38579436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington’s 2nd legislative district |
E929499
|
entity |
| Predicate | numberOfStateRepresentativesElected |
P4273
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Washington’s 2nd legislative district, numberOfStateRepresentativesElected, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStateRepresentativesElected Context triple: [Washington’s 2nd legislative district, numberOfStateRepresentativesElected, 2]
-
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.
numberOfElectors
Indicates the total count of electors associated with a given entity or context.
-
D.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or context.
-
E.
electRepresentativesTo
Indicates the action by which a group or body chooses individuals to serve as its official representatives in another governing or decision-making body.
- 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_69f76ebd2248819083978362d81fa35e |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:32 p.m.