Triple
T13716279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zone 7 Water Agency |
E328906
|
entity |
| Predicate | hasTypeOfCustomer |
P809
|
FINISHED |
| Object | retail water agencies |
—
|
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: retail water agencies | Statement: [Zone 7 Water Agency, hasTypeOfCustomer, retail water agencies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfCustomer Context triple: [Zone 7 Water Agency, hasTypeOfCustomer, retail water agencies]
-
A.
customerType
chosen
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
B.
hasSecondaryCustomerType
Indicates that an entity is associated with an additional, non-primary customer classification or role.
-
C.
hasTypeOfCredit
Indicates that an entity is associated with or characterized by a specific type or category of credit.
-
D.
underlyingCompanyCustomerType
Indicates the type or category of customer relationship that an underlying company has (e.g., retail, institutional, corporate).
-
E.
hasStakeholderType
Indicates that an entity is associated with a stakeholder and specifies the category or role that stakeholder fulfills in relation to the entity.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4398f0448190810c840a82228706 |
completed | April 13, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:54 p.m.