Triple
T7648585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1994 United States House of Representatives elections |
E173187
|
entity |
| Predicate | RepublicanSeatsAfter |
P4273
|
FINISHED |
| Object | 230 |
—
|
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: 230 | Statement: [1994 United States House of Representatives elections, RepublicanSeatsAfter, 230]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: RepublicanSeatsAfter Context triple: [1994 United States House of Representatives elections, RepublicanSeatsAfter, 230]
-
A.
numberOfRepresentatives
chosen
Indicates the quantity of representatives associated with a given entity or unit.
-
B.
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.
-
C.
numberOfSenates
Indicates the total count of senate bodies associated with or present in a given context or entity.
-
D.
majoritySeatsCongress
Indicates that a political party or group holds more than half of the seats in a country's congress or legislative body.
-
E.
seatsWonByNewDemocraticParty
Indicates the number of electoral seats that were won by the New Democratic Party in a given election or governing 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_69c6995473348190a4f41d110d619a18 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c6fb134a40819097f9de5f24d1df0f |
completed | March 27, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e9ef1c81909c8bff716541ac1f |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:58 p.m.