Triple
T15938769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayraktar TB2 |
E386505
|
entity |
| Predicate | designer |
P184
|
FINISHED |
| Object | Baykar |
E1185416
|
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: Baykar | Statement: [Bayraktar TB2, designer, Baykar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baykar Context triple: [Bayraktar TB2, designer, Baykar]
-
A.
Baykar
chosen
Baykar is a Turkish defense and aerospace company best known for developing and producing the Bayraktar family of unmanned combat aerial vehicles (UCAVs).
-
B.
Turkish Aerospace Industries
Turkish Aerospace Industries is Turkey’s leading aerospace and defense company, specializing in the design, modernization, and production of military and civilian aircraft, helicopters, UAVs, and space systems.
-
C.
Nord Aviation
Nord Aviation was a French aerospace manufacturer known for producing military and civil aircraft and later becoming part of Aérospatiale.
-
D.
Bölkow
Bölkow was a German aerospace company known for its development of helicopters and aircraft, which later became part of the larger conglomerate Messerschmitt-Bölkow-Blohm (MBB).
-
E.
Aero Vodochody
Aero Vodochody is a Czech aerospace company best known for designing and producing military jet trainers and light combat aircraft, including the L-39 Albatros.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156ac934c8190b6178eb66023252e |
completed | April 16, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe7455c48190bfad24eb8905426d |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:53 a.m.