Triple
T227049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chamber of Deputies of Mexico |
E4334
|
entity |
| Predicate | totalSingleMemberDistrictSeats |
P1679
|
FINISHED |
| Object | 300 |
—
|
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: 300 | Statement: [Chamber of Deputies of Mexico, totalSingleMemberDistrictSeats, 300]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalSingleMemberDistrictSeats Context triple: [Chamber of Deputies of Mexico, totalSingleMemberDistrictSeats, 300]
-
A.
numberOfDistricts
chosen
Indicates the total count of districts associated with a given entity or area.
-
B.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given entity.
-
C.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
D.
numberOfSeatsWonIn2019ParliamentaryElection
Indicates the number of seats an entity won in the 2019 parliamentary election.
-
E.
seatsWonIn2019ParliamentaryElection
Indicates the number of seats an entity secured in the 2019 parliamentary 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d10ac248190a98dedabf5358668 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b5877588190af694d060377f027 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.