Triple
T22713457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercure |
E561661
|
entity |
| Predicate | latinName |
P3646
|
FINISHED |
| Object | Mercurius |
—
|
NE NERFINISHED |
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: Mercurius | Statement: [Mercure, latinName, Mercurius]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mercurius Context triple: [Mercure, latinName, Mercurius]
-
A.
Mercury (Roman god)
chosen
Mercury is the Roman god of commerce, communication, and travel, often depicted as a swift messenger of the gods and protector of merchants and thieves.
-
B.
Mercurio
Mercurio is a Latin American boy band modeled after and musically influenced by the pioneering teen pop group Menudo.
-
C.
Mercuri
Mercuri is the surname of Brazilian singer, songwriter, and performer Daniela Mercury, a prominent figure in axé and pop music.
-
D.
Merkur Scorpio
The Merkur Scorpio is a mid-size executive car produced by Ford's German Merkur brand in the late 1980s, known for its advanced features and comfortable ride.
-
E.
Mercury
Mercury was an American automobile marque of the Ford Motor Company known for producing mid-priced cars positioned between Ford and Lincoln.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2454f1348819088d83f420925a5c1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1790ab6208190a342f076002324ab |
completed | April 29, 2026, 3:20 a.m. |
Created at: April 17, 2026, 3:18 p.m.