Triple
T227050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chamber of Deputies of Mexico |
E4334
|
entity |
| Predicate | totalProportionalRepresentationSeats |
P4273
|
FINISHED |
| Object | 200 |
—
|
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: 200 | Statement: [Chamber of Deputies of Mexico, totalProportionalRepresentationSeats, 200]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalProportionalRepresentationSeats Context triple: [Chamber of Deputies of Mexico, totalProportionalRepresentationSeats, 200]
-
A.
apportionedBy
Indicates that something is divided or allocated among parts or recipients according to a specified agent, rule, or method.
-
B.
seatsWonIn2019ParliamentaryElection
Indicates the number of seats an entity secured in the 2019 parliamentary election.
-
C.
numberOfSeatsWonIn2019ParliamentaryElection
Indicates the number of seats an entity won in the 2019 parliamentary election.
-
D.
numberOfRepresentatives
chosen
Indicates the quantity of representatives associated with a given entity or unit.
-
E.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to a given entity or context.
- 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.