Triple
T22353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California State Senate |
E443
|
entity |
| Predicate | numberOfDistricts |
P1679
|
FINISHED |
| Object | 40 |
—
|
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: 40 | Statement: [California State Senate, numberOfDistricts, 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDistricts Context triple: [California State Senate, numberOfDistricts, 40]
-
A.
hasCouncilDistrict
Indicates that an entity is located within or represented by a specific council district.
-
B.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
C.
numberOfHouses
Indicates the quantity of houses associated with a given entity or context.
-
D.
divisionTitle
Indicates the formal name or title assigned to a specific division within a larger organization or structure.
-
E.
federalDistrictCreatedBy
Indicates that a federal district was established or brought into existence through the action, decision, or authority of a specified creator (such as a government body, law, or decree).
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a246e94ca881908f7a7d2c0b293033 |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a24654724481909ba14b7f68d2a472 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a246e7fac481909b0c500d4500650e |
completed | Feb. 28, 2026, 1:37 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.