Triple
T29974164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sepang (federal constituency) |
E761397
|
entity |
| Predicate | numberOfPollingDistricts |
P201890
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [Sepang (federal constituency), numberOfPollingDistricts, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPollingDistricts Context triple: [Sepang (federal constituency), numberOfPollingDistricts, 30]
-
A.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
-
B.
havePollingStations
Indicates that one entity possesses, hosts, or is assigned a set of polling stations for conducting an election or vote.
-
C.
numberOfPrecincts
Indicates the total count of precincts associated with a given entity or jurisdiction.
-
D.
eachDistrictElects
Indicates that every electoral district selects or chooses its own representative or set of representatives.
-
E.
numberOfDistrictMembers
Indicates the relationship that specifies how many members are associated with a given district.
- 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_69f22467626081908d5afea489590e96 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_6a002f0839fc8190a874d3b0d0826d7e |
completed | May 10, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_6a002eae7b6481909974b321e2789b7e |
completed | May 10, 2026, 7:07 a.m. |
| PDg | Predicate description generation | batch_6a002f071de88190b1fa4f5531a6cea7 |
completed | May 10, 2026, 7:08 a.m. |
Created at: April 29, 2026, 6:33 p.m.