Triple
T14522703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamov Ka-31 |
E340691
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Kamov |
E1075306
|
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: Kamov | Statement: [Kamov Ka-31, manufacturer, Kamov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamov Context triple: [Kamov Ka-31, manufacturer, Kamov]
-
A.
Kamov
chosen
Kamov is a Russian aerospace company renowned for designing and producing coaxial-rotor helicopters, particularly for naval and military use.
-
B.
Kamov Ka-25
The Kamov Ka-25 is a Soviet-era naval helicopter designed primarily for anti-submarine warfare operations from warships.
-
C.
Kazan Helicopters
Kazan Helicopters is a major Russian helicopter design and manufacturing company known for producing a wide range of civilian and military rotorcraft.
-
D.
Kamov Ka-29
The Kamov Ka-29 is a Soviet/Russian shipborne assault and transport helicopter developed from the Ka-27, designed primarily for amphibious assault and troop support operations.
-
E.
Antonov
Antonov is a Ukrainian aerospace company best known for designing and producing large cargo and passenger aircraft, including some of the world’s heaviest and largest planes.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea04f16f88190ba357b0f8021b46b |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a4da6908190a3e2cae16f6240d9 |
completed | May 8, 2026, 5:53 a.m. |
Created at: April 10, 2026, 1:22 a.m.