Triple
T1183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington, D.C. |
E23
|
entity |
| Predicate | hasElectoralVotes |
P280
|
FINISHED |
| Object | 3 presidential electors |
—
|
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: 3 presidential electors | Statement: [Washington, D.C., hasElectoralVotes, 3 presidential electors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasElectoralVotes Context triple: [Washington, D.C., hasElectoralVotes, 3 presidential electors]
-
A.
hasElectoralRepresentation
Indicates that one entity is represented in an electoral body or decision-making institution by another entity (such as a representative, party, or delegation).
-
B.
elects
Indicates that one entity selects or chooses another entity for a position, role, or office, typically through a formal voting process.
-
C.
hasPresident
Indicates that an entity holds the position or role of president for another entity.
-
D.
hasLieutenantGovernor
Indicates that one entity serves as the lieutenant governor of another entity (typically a state, province, or territory).
-
E.
hasMayor
Indicates that one entity serves as the mayor of another entity, typically a city, town, or municipality.
- 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_69a22a285828819081a58308fb963df1 |
completed | Feb. 27, 2026, 11:35 p.m. |
| NER | Named-entity recognition | batch_69a23344daf8819083118bbac5f46568 |
completed | Feb. 28, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69a232e52e7c81909c072703e28e8c61 |
completed | Feb. 28, 2026, 12:12 a.m. |
| PDg | Predicate description generation | batch_69a233443224819097b91b150fdfbd1a |
completed | Feb. 28, 2026, 12:13 a.m. |
Created at: Feb. 27, 2026, 11:36 p.m.