Triple
T10020662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Thomas Assembly |
E200601
|
entity |
| Predicate | notableUseOfProducts |
P5773
|
FINISHED |
| Object | police interceptor vehicles |
—
|
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: police interceptor vehicles | Statement: [St. Thomas Assembly, notableUseOfProducts, police interceptor vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableUseOfProducts Context triple: [St. Thomas Assembly, notableUseOfProducts, police interceptor vehicles]
-
A.
notableUse
chosen
Indicates that something is prominently or famously used by a particular entity, context, or for a specific purpose.
-
B.
notableProduct
Indicates that a product is especially significant, prominent, or well-known in relation to the associated entity.
-
C.
isFamouslyUsedBy
Indicates that something is widely and notably used by a particular person, group, or entity, in a way that is broadly recognized or associated with them.
-
D.
notableProductType
Indicates that an entity is particularly well-known or distinguished for producing or offering a specific type of product.
-
E.
isFamouslyUsedIn
Indicates that something is widely recognized or well-known for being used in a particular context, work, or situation.
- 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_69ca831c45f08190ac1505cc15076608 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd777b208190ad75eac79eec0c2f |
completed | April 2, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69cd4b7cd4208190b2253583ee2f892c |
completed | April 1, 2026, 4:44 p.m. |
Created at: March 30, 2026, 8:53 p.m.