Triple
T8423911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ASCOD program |
E198928
|
entity |
| Predicate | associatedCountryVehicleName |
P55902
|
FINISHED |
| Object | Pizarro for Spain |
—
|
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: Pizarro for Spain | Statement: [ASCOD program, associatedCountryVehicleName, Pizarro for Spain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCountryVehicleName Context triple: [ASCOD program, associatedCountryVehicleName, Pizarro for Spain]
-
A.
associatedCountry
Indicates that there is a relevant connection or linkage between an entity and a specific country, such as origin, operation, or affiliation.
-
B.
identifiesVehiclesRegisteredIn
Indicates that an entity specifies or determines which vehicles are registered within a particular scope or authority.
-
C.
vehicleName
chosen
Indicates the specific name or designation assigned to a vehicle.
-
D.
countryOfRegistry
Indicates the country in which an entity (such as a ship, aircraft, or company) is officially registered.
-
E.
brandUsedInCountry
Indicates that a particular brand is used or present within a specified country.
- 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_69ca8312d63c8190bf133b676b44a385 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb859f787481908a11797a317c8849 |
completed | March 31, 2026, 8:28 a.m. |
| PD | Predicate disambiguation | batch_69cb70d70ea081909c3dc1bd2ec14f85 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:07 p.m.