Triple
T621133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Representatives of Puerto Rico |
E14514
|
entity |
| Predicate | numberOfAtLargeSeats |
P17214
|
FINISHED |
| Object | 11 |
—
|
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: 11 | Statement: [House of Representatives of Puerto Rico, numberOfAtLargeSeats, 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAtLargeSeats Context triple: [House of Representatives of Puerto Rico, numberOfAtLargeSeats, 11]
-
A.
numberOfSeatsContested
Indicates the total count of seats in an election or contest that are being competed for or are up for selection.
-
B.
numberOfElectors
Indicates the total count of electors associated with a given entity or context.
-
C.
numberOfRepresentatives
Indicates the quantity of representatives associated with a given entity or unit.
-
D.
hasNonvotingMember
Indicates that an entity is associated with a member who participates in some capacity but does not possess formal voting rights in the relevant decision-making process.
-
E.
numberOfCommitteeMembers
Indicates the total count of individuals who are members of a given committee.
- 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_69a4934b17c881909ace8270e8ddd202 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e3e5d80819096e72e11b533f931 |
completed | March 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69a49cfe9bc081909a01b4b3b48f03b7 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49defe58c8190bd39ef47c9f660a7 |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:35 p.m.