Triple
T25464828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | California State Water Resources Control Board |
E638144
|
entity |
| Predicate | numberOfSubAgencies |
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, numberOfSubAgencies, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSubAgencies Context triple: [California State Water Resources Control Board, numberOfSubAgencies, 9]
-
A.
numberOfSubordinateAgencies
chosen
Indicates the total count of agencies that are hierarchically subordinate to a given parent agency.
-
B.
hasNumberOfAgencies
Indicates the quantity of agencies associated with or linked to a given entity.
-
C.
numberOfOffices
Indicates the total count of offices associated with a given entity.
-
D.
numberOfTargetInstitutions
Indicates the count of institutions that are designated or identified as targets in a given context or dataset.
-
E.
numberOfSubcamps
Indicates the total count of subordinate or subsidiary camps associated with a main camp.
- 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_69e75db8bab08190baca80b4a8c315fd |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f7764ab1fc81909f9348db87bd7692 |
completed | May 3, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69f76905d9c88190b1ee810bc9ab644f |
completed | May 3, 2026, 3:25 p.m. |
Created at: April 21, 2026, 2:14 p.m.