Triple
T10500243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States federal electoral districts |
E247652
|
entity |
| Predicate | numberOfSeatsFixedSince |
P63784
|
FINISHED |
| Object | 1913 |
—
|
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: 1913 | Statement: [United States federal electoral districts, numberOfSeatsFixedSince, 1913]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSeatsFixedSince Context triple: [United States federal electoral districts, numberOfSeatsFixedSince, 1913]
-
A.
totalSeatsFixedSince
chosen
Indicates the point in time since which the total number of seats has remained fixed and unchanged.
-
B.
previousNumberOfSeats
Indicates the number of seats an entity had before a change or update in its seating count.
-
C.
currentNumberOfSeats
Indicates the present total count of seats associated with an entity or context.
-
D.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
-
E.
individualSeats
Indicates that an entity provides or consists of separate, single-person seating positions rather than shared or bench-style seating.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5098ef57c8190a9803c2c1736d299 |
completed | April 7, 2026, 1:41 p.m. |
| PD | Predicate disambiguation | batch_69d4fb8e24ac8190912c9f11b8bd3084 |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:25 p.m.