Triple
T12341513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KAMAZ |
E294236
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | KAMAZ-master |
E294236
|
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: KAMAZ-master | Statement: [KAMAZ, hasPart, KAMAZ-master]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KAMAZ-master Context triple: [KAMAZ, hasPart, KAMAZ-master]
-
A.
KAMAZ
chosen
KAMAZ is a major Russian truck manufacturer known for producing heavy-duty vehicles and achieving multiple victories in the Dakar Rally.
-
B.
Kamionna
Kamionna is a village located in the Beskid Wyspowy mountain range in southern Poland, known for its scenic, hilly landscape.
-
C.
Kamionek
Kamionek is a district in Warsaw, Poland, historically known as an industrial and working-class area on the eastern bank of the Vistula River.
-
D.
Omsktransmash
Omsktransmash is a major Russian tank and armored vehicle manufacturer based in Omsk, historically known for producing Soviet-era main battle tanks.
-
E.
Kurgan Bus Plant
Kurgan Bus Plant is a Russian manufacturing company specializing in the production of buses and related automotive vehicles.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7758dc8190bbc6a9ad00b01dce |
completed | April 10, 2026, 6:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62aaa1d548190be065412aab70385 |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:53 p.m.