Triple
T31112768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frances |
E792996
|
entity |
| Predicate | countryOfOriginOfDesignatedAircraft |
P68259
|
FINISHED |
| Object | Japan |
—
|
NE NERFINISHED |
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: Japan | Statement: [Frances, countryOfOriginOfDesignatedAircraft, Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfOriginOfDesignatedAircraft Context triple: [Frances, countryOfOriginOfDesignatedAircraft, Japan]
-
A.
aircraftOriginCountry
chosen
Indicates the country from which an aircraft originates, such as where it was built, registered, or primarily associated.
-
B.
operatesAircraftRegistrationCountry
Indicates that an entity operates or uses an aircraft that is registered in a specified country.
-
C.
eraOfDesignatedAircraft
Indicates the historical time period or era during which the designated aircraft was primarily designed, developed, or in standard operational use.
-
D.
appliedToAircraftDesignedBy
Indicates that something (such as a component, system, or regulation) is applied to an aircraft that was designed by a specified designer or organization.
-
E.
appliedToAircraftBuiltBy
Indicates that something (such as a regulation, modification, or action) is applied specifically to aircraft that were built by a particular manufacturer or builder.
- 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_69f224cfd5d881908ec6447bc321cd58 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69ffc704e1e88190884928a6a5c55a87 |
completed | May 9, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69ffc6b483d881908ad872e25fa6abc5 |
completed | May 9, 2026, 11:43 p.m. |
Created at: April 29, 2026, 9:04 p.m.