Triple
T377902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merkur XR4Ti |
E8610
|
entity |
| Predicate | marketedBy |
P4613
|
FINISHED |
| Object | Merkur |
E8610
|
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: Merkur | Statement: [Merkur XR4Ti, marketedBy, Merkur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Merkur Context triple: [Merkur XR4Ti, marketedBy, Merkur]
-
A.
Merkur
chosen
Merkur was a short-lived automotive marque created by Ford in the 1980s to sell European-designed performance and luxury cars in the North American market.
-
B.
Mercury
Mercury was an American automobile marque of the Ford Motor Company known for producing mid-priced cars positioned between Ford and Lincoln.
-
C.
Mercury
Mercury is the smallest and innermost planet in our Solar System, known for its extreme temperature variations and heavily cratered surface.
-
D.
Venus
Venus is the second planet from the Sun, known for its dense, toxic atmosphere, extreme surface temperatures, and bright visibility in Earth's sky.
-
E.
Venera
Venera is a grade or class within the Mexican Order of the Aztec Eagle, the country’s highest distinction awarded to foreigners.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec1804108190a1e94526b71289ea |
completed | Feb. 28, 2026, 1:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3f4ded68c8190b6951c03fd75c404 |
completed | March 1, 2026, 8:12 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.