Triple
T11675950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2019 New South Wales state election |
E277490
|
entity |
| Predicate | numberOfLegislativeCouncilSeatsContested |
P11588
|
FINISHED |
| Object | 21 |
—
|
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: 21 | Statement: [2019 New South Wales state election, numberOfLegislativeCouncilSeatsContested, 21]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLegislativeCouncilSeatsContested Context triple: [2019 New South Wales state election, numberOfLegislativeCouncilSeatsContested, 21]
-
A.
legislativeCouncilSeats
Indicates the number of seats held or allocated in a legislative council within a given political or administrative context.
-
B.
numberOfSeatsContested
chosen
Indicates the total count of seats in an election or contest that are being competed for or are up for selection.
-
C.
numberOfElectorates
Indicates the total count of electoral districts or constituencies associated with a given entity.
-
D.
typeOfElectionContested
Indicates the specific kind or category of election in which an entity is competing or has competed.
-
E.
legislativeAssemblySeats
Indicates the number of seats an entity holds or is allocated in a legislative assembly.
- 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_69d6aafd0a448190b44da30af8c6c519 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a44504c48190b519765a83ff9c5e |
completed | April 10, 2026, 7:18 a.m. |
| PD | Predicate disambiguation | batch_69d88a77e6e88190b7519100bde76575 |
completed | April 10, 2026, 5:28 a.m. |
Created at: April 8, 2026, 9:40 p.m.