Triple
T7053827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California State Water Resources Control Board |
E164034
|
entity |
| Predicate | numberOfSubordinateAgencies |
P74767
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [California State Water Resources Control Board, numberOfSubordinateAgencies, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSubordinateAgencies Context triple: [California State Water Resources Control Board, numberOfSubordinateAgencies, 9]
-
A.
numberOfCountryOffices
Indicates the total count of offices or branches that an organization maintains across different countries.
-
B.
numberOfMinistries
Indicates the total count of ministries associated with or belonging to a given entity.
-
C.
federalSubjectCount
Indicates the number of federal subjects (administrative units within a federation) associated with or contained by an entity.
-
D.
numberOfOffices
Indicates the total count of offices associated with a given entity.
-
E.
hasNumberOfSubcommittees
Indicates the relationship that specifies how many subcommittees are associated with a given entity.
- 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_69c68861678881909961ddf4d779f750 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e4a3c36c819080942c59f1830ae8 |
completed | March 27, 2026, 8:12 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bdc1f08190975fcdbbb1854d1e |
completed | March 27, 2026, 7:59 p.m. |
| PDg | Predicate description generation | batch_69c6e4a15b088190bee9a23e94aaac53 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:37 p.m.