Triple
T13290171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AMG Line |
E316541
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Mercedes-AMG |
E1020713
|
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: Mercedes-AMG | Statement: [AMG Line, relatedTo, Mercedes-AMG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mercedes-AMG Context triple: [AMG Line, relatedTo, Mercedes-AMG]
-
A.
Mercedes-AMG
chosen
Mercedes-AMG is the high-performance division of Mercedes-Benz, specializing in sport-tuned luxury vehicles and powerful engines.
-
B.
Mercedes
Mercedes is a courageous and compassionate housekeeper who secretly aids the Spanish Maquis resistance in Guillermo del Toro’s dark fantasy film "Pan’s Labyrinth."
-
C.
Mercedes
Mercedes is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and fishing-based local economy.
-
D.
Mercedes
Mercedes is a minor but memorable character in Jack London’s novel "The Call of the Wild," portrayed as a pampered, naive woman whose behavior contributes to the hardship and downfall of her sledding party.
-
E.
Mercedes
Mercedes is a coastal municipality in the Philippine province of Camarines Norte known for its fishing industry and nearby island attractions.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99077a8f48190b1163448a3a978a2 |
completed | April 11, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7266832a881909403c4d3cbe1edec |
completed | May 3, 2026, 10:41 a.m. |
Created at: April 9, 2026, 9:27 p.m.