Triple
T280105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Petit & Fritsen |
E5333
|
entity |
| Predicate | customers |
P7793
|
FINISHED |
| Object | churches |
—
|
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: churches | Statement: [Petit & Fritsen, customers, churches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: customers Context triple: [Petit & Fritsen, customers, churches]
-
A.
customerType
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
B.
majorCustomer
Indicates that one entity is a primary or high-value customer of another entity, typically contributing a significant portion of business or revenue.
-
C.
buyer
chosen
Indicates a relationship where one entity purchases or acquires goods, services, or rights from another entity in exchange for payment or compensation.
-
D.
user
Indicates a relationship where an entity actively operates, controls, or interacts with another entity, typically as the primary agent or consumer of its function.
-
E.
custom
Indicates that something is specially created, configured, or tailored for a particular purpose, context, or user rather than being standard or generic.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25e0868708190ad551ca06cc57f4a |
completed | Feb. 28, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69a25b765f488190b2cbe4b45cd42821 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.