Triple
T4399157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Senate from North Carolina |
E99569
|
entity |
| Predicate | currentNumberOfSeats |
P55873
|
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: [U.S. Senate from North Carolina, currentNumberOfSeats, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currentNumberOfSeats Context triple: [U.S. Senate from North Carolina, currentNumberOfSeats, 2]
-
A.
previousNumberOfSeats
Indicates the number of seats an entity had before a change or update in its seating count.
-
B.
seatingCapacity
Indicates the maximum number of people that something (typically a venue or vehicle) is designed or allowed to seat.
-
C.
hasSeatStatus
Indicates the current condition or availability state of a seat in a given context.
-
D.
remainingSeatsAllocation
Indicates how any seats that are still unassigned after an initial distribution are allocated among eligible parties or entities.
-
E.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
- 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_69b345506b408190b0e3dee616738a7d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352cc4ab081908bc45d2f76cd4da8 |
completed | March 12, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69b34f597998819092477efdedb51427 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff654308190b9717526120d80d3 |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:20 p.m.