Triple
T10176680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BAe Jetstream 31 |
E235868
|
entity |
| Predicate | registrationPrefixExamples |
P40413
|
FINISHED |
| Object | G- (United Kingdom) |
—
|
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: G- (United Kingdom) | Statement: [BAe Jetstream 31, registrationPrefixExamples, G- (United Kingdom)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: registrationPrefixExamples Context triple: [BAe Jetstream 31, registrationPrefixExamples, G- (United Kingdom)]
-
A.
registrationPrefix
Indicates that an entity has a specific registration prefix code assigned to it as part of its official registration or identification.
-
B.
addressExamplePrefix
Indicates that an address example is introduced or categorized by a specific prefix string used to distinguish or group it.
-
C.
registrationSuffix
Indicates the trailing part or extension added to a base registration identifier to form a complete registration code.
-
D.
namePrefix
Indicates that one entity is a prefix or leading part of another entity’s name.
-
E.
civilRegistrationPrefixExample
chosen
Indicates that there is an example of a standard prefix used in civil registration identifiers or records.
- 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_69ca84d1d5f88190ab878a1021ecff68 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdecd3a2688190bce277bffffcbf8b |
completed | April 2, 2026, 4:13 a.m. |
| PD | Predicate disambiguation | batch_69cd7c79f21c8190a7f31b2eab80b8ba |
completed | April 1, 2026, 8:13 p.m. |
Created at: March 30, 2026, 9:11 p.m.