Triple
T10908624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merkur (automobile marque) |
E257633
|
entity |
| Predicate | competitor |
P1375
|
FINISHED |
| Object | Volvo |
E83041
|
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: Volvo | Statement: [Merkur (automobile marque), competitor, Volvo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Volvo Context triple: [Merkur (automobile marque), competitor, Volvo]
-
A.
Volvo Cars
chosen
Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
-
B.
Volvo Group
Volvo Group is a Swedish multinational manufacturing company best known for producing trucks, buses, construction equipment, and marine and industrial engines.
-
C.
VOLV A
VOLV A is a class of Volvo Group shares traded on the Nasdaq Stockholm stock exchange.
-
D.
Scania
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
-
E.
Scania
Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7706824d08190ba894d144cc6b3ba |
completed | April 9, 2026, 9:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e23baecb008190b74bcbad1db61a91 |
completed | April 17, 2026, 1:54 p.m. |
Created at: April 8, 2026, 9:22 p.m.