Triple
T9663933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fokker Aerostructures |
E233655
|
entity |
| Predicate | parentCompany |
P254
|
FINISHED |
| Object | GKN Fokker |
E387293
|
NE 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: GKN Fokker | Statement: [Fokker Aerostructures, parentCompany, GKN Fokker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GKN Fokker Context triple: [Fokker Aerostructures, parentCompany, GKN Fokker]
-
A.
Fokker Aerostructures
Fokker Aerostructures is a Dutch aerospace company specializing in the design and manufacture of advanced aircraft structures and components for civil and military programs.
-
B.
Fokker
chosen
Fokker is a historic Dutch aerospace company best known for designing and manufacturing civil and military aircraft.
-
C.
Hawker Siddeley
Hawker Siddeley was a major British aircraft manufacturing and engineering conglomerate known for producing military and civil aircraft during the mid-20th century.
-
D.
Avro
Avro is a row-oriented, schema-based data serialization format commonly used in big data processing and storage systems.
-
E.
Avro
Avro was a British aircraft manufacturer best known for producing iconic military aircraft such as the Avro Lancaster bomber during the 20th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca848d3b6c8190ae98ea554dea58df |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9c0cde048190b5a8e1548825d4d9 |
completed | April 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18a17eea08190825404b01b0e8d96 |
completed | April 4, 2026, 10 p.m. |
Created at: March 30, 2026, 8:14 p.m.