Triple

T23758146
Position Surface form Disambiguated ID Type / Status
Subject Klimov TV3-117 E587175 entity
Predicate usedIn P98 FINISHED
Object Kamov Ka-50
The Kamov Ka-50 is a Russian single-seat, coaxial-rotor attack helicopter designed for high agility and heavy firepower in frontline combat roles.
 E1627132 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 Ka-50 | Statement: [Klimov TV3-117, usedIn, Kamov Ka-50]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kamov Ka-50
Triple: [Klimov TV3-117, usedIn, Kamov Ka-50]
Generated description
The Kamov Ka-50 is a Russian single-seat, coaxial-rotor attack helicopter designed for high agility and heavy firepower in frontline combat roles.

Provenance (5 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_69e2490a0eec81908cdef8a862828d7a completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1bdad46888190b438b22985838640 completed April 29, 2026, 8:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc98f70288190bc2b3896d4642f30 completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fcb41ec208190ab3fb4e8b52c8c81 completed May 22, 2026, 3:19 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcbb4883c81909bb9f02361c69fe7 completed May 22, 2026, 3:21 a.m.
Created at: April 17, 2026, 7:14 p.m.