Triple
T14375397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AutoScout24 GmbH |
E356460
|
entity |
| Predicate | operatesWebsite |
P5884
|
FINISHED |
| Object | autoscout24.com |
E356456
|
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: autoscout24.com | Statement: [AutoScout24 GmbH, operatesWebsite, autoscout24.com]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: autoscout24.com Context triple: [AutoScout24 GmbH, operatesWebsite, autoscout24.com]
-
A.
AutoScout24
chosen
AutoScout24 is a major European online marketplace specializing in buying and selling new and used cars.
-
B.
AutoScout24 GmbH
AutoScout24 GmbH is a major European online marketplace specializing in the listing and trading of new and used vehicles.
-
C.
Autotrader
Autotrader is a major online automotive marketplace where consumers can buy, sell, and research new and used vehicles.
-
D.
Cazoo
Cazoo is a UK-based online car retailer known for its high-profile sports sponsorships and rapid growth in the digital automotive marketplace.
-
E.
Araba
Araba is the Basque-language name for Álava, a historic province in northern Spain that forms part of the Basque Country.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de900949fc81909be0da1734c46645 |
completed | April 14, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c5514688190be90776c8764d4f3 |
completed | May 8, 2026, 2:37 a.m. |
Created at: April 10, 2026, 1:16 a.m.