Triple
T37826923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2016 United States House of Representatives elections |
E943086
|
entity |
| Predicate | totalSeatsInHouse |
P135503
|
FINISHED |
| Object | 435 |
—
|
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: 435 | Statement: [2016 United States House of Representatives elections, totalSeatsInHouse, 435]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalSeatsInHouse Context triple: [2016 United States House of Representatives elections, totalSeatsInHouse, 435]
-
A.
numberOfSeatsInHouseOfCommons
Indicates the total count of seats allocated in the House of Commons.
-
B.
numberOfSeatsInSenate
Indicates the total count of seats allocated in a given senate.
-
C.
numberOfTermsInHouse
Indicates the total count of terms an individual has served in a legislative house.
-
D.
seatsComparedToMembers
Indicates the relationship between the number of seats available and the number of members, typically expressing how the seat count compares to membership size.
-
E.
typicalHouseSeatCount
chosen
Indicates the usual or standard number of seats found in a typical house.
- 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_69f76eea4c8c8190a335aed5955cf2db |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fd2a215d6c8190a1a428ccaee603f1 |
completed | May 8, 2026, 12:11 a.m. |
| PD | Predicate disambiguation | batch_69fd28ef19688190bb8370f2812a43e7 |
completed | May 8, 2026, 12:06 a.m. |
Created at: May 3, 2026, 4:19 p.m.