Triple
T2739886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tatarstan |
E60722
|
entity |
| Predicate | hasCompany |
P1287
|
FINISHED |
| Object |
KAMAZ
KAMAZ is a major Russian truck manufacturer known for producing heavy-duty vehicles and achieving multiple victories in the Dakar Rally.
|
E294236
|
NE FINISHED |
How this triple was built (4 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 | Statement: [Tatarstan, hasCompany, KAMAZ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KAMAZ Context triple: [Tatarstan, hasCompany, KAMAZ]
-
A.
Traktor Chelyabinsk
Traktor Chelyabinsk is a professional ice hockey club from Chelyabinsk, Russia, historically recognized as one of the prominent teams in Soviet and Russian hockey.
-
B.
Traktor Stalingrad
Traktor Stalingrad was a Soviet-era football club from the city then known as Stalingrad, later renamed FC Rotor Volgograd.
-
C.
Hino
Hino is a town in Shiga Prefecture, Japan, known for its historical streetscapes and traditional industries.
-
D.
Hino
Hino is a city in western Tokyo, Japan, known as a residential and industrial suburb within the Tama area.
-
E.
GAZ Group
GAZ Group is a major Russian automotive manufacturer best known for producing commercial vehicles, trucks, and buses.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: KAMAZ Triple: [Tatarstan, hasCompany, KAMAZ]
Generated description
KAMAZ is a major Russian truck manufacturer known for producing heavy-duty vehicles and achieving multiple victories in the Dakar Rally.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KAMAZ Target entity description: KAMAZ is a major Russian truck manufacturer known for producing heavy-duty vehicles and achieving multiple victories in the Dakar Rally.
-
A.
Traktor Chelyabinsk
Traktor Chelyabinsk is a professional ice hockey club from Chelyabinsk, Russia, historically recognized as one of the prominent teams in Soviet and Russian hockey.
-
B.
Traktor Stalingrad
Traktor Stalingrad was a Soviet-era football club from the city then known as Stalingrad, later renamed FC Rotor Volgograd.
-
C.
Hino
Hino is a town in Shiga Prefecture, Japan, known for its historical streetscapes and traditional industries.
-
D.
Hino
Hino is a city in western Tokyo, Japan, known as a residential and industrial suburb within the Tama area.
-
E.
GAZ Group
GAZ Group is a major Russian automotive manufacturer best known for producing commercial vehicles, trucks, and buses.
- F. None of above. chosen
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_69ab4b77febc819095603eb012cd141b |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb2da94c8190bc9d23262e3dfc07 |
completed | March 7, 2026, 8 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbc828388190af6c3b75ed765401 |
completed | March 10, 2026, 6:35 a.m. |
| NEDg | Description generation | batch_69afbc3b0aa08190ac0254e7e4300b9c |
completed | March 10, 2026, 6:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afbcb25ee4819093a8bd77bbcd8a86 |
completed | March 10, 2026, 6:39 a.m. |
Created at: March 6, 2026, 9:56 p.m.