Triple
T38047371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2006 Canadian federal election |
E949652
|
entity |
| Predicate | secondPartySeatCount |
P128078
|
FINISHED |
| Object | 103 |
—
|
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: 103 | Statement: [2006 Canadian federal election, secondPartySeatCount, 103]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: secondPartySeatCount Context triple: [2006 Canadian federal election, secondPartySeatCount, 103]
-
A.
secondPartySeats
Indicates that a second party assigns or provides seating or seats to another entity.
-
B.
party2Seats
chosen
Indicates the number of seats held or allocated to the second party in a given context (such as an election or governing body).
-
C.
thirdPartySeatCount
Indicates the number of seats held by political parties other than the primary or main parties in a given context.
-
D.
hasSecondSeat
Indicates that an entity possesses or includes a secondary seat in addition to a primary one.
-
E.
secondLargestPartySeatsWon
Indicates the number of seats won by the political party that finished second in size or vote share in an election.
- 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_69f76f000cf081908c11fb5443b392e6 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ffc605b0648190a7abe9128b0d857a |
completed | May 9, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69ffc5742d80819099f947ece78d5700 |
completed | May 9, 2026, 11:38 p.m. |
Created at: May 3, 2026, 4:20 p.m.