Triple
T13442887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercédès Jellinek |
E320408
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mercédès |
E320408
|
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: Mercédès | Statement: [Mercédès Jellinek, givenName, Mercédès]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mercédès Context triple: [Mercédès Jellinek, givenName, Mercédès]
-
A.
Mercédès
Mercédès is a central character in Alexandre Dumas' novel "The Count of Monte Cristo," known as Edmond Dantès' former fiancée whose life is upended by his wrongful imprisonment and presumed death.
-
B.
Mercédès
chosen
Mercédès is the given name of Mercédès Jellinek, the woman after whom the Mercedes automobile brand was named.
-
C.
Magaro
Magaro is an Italian-origin surname most notably borne by American actor John Magaro.
-
D.
L’Auto
L’Auto was a French sports newspaper best known for creating and organizing the Tour de France.
-
E.
Lagonda
Lagonda is a historic British luxury car marque renowned for its high-performance grand tourers and association with Aston Martin.
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaee881888190811ddf01bc699864 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f739965ef081909e85881ce805bbb5 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:40 p.m.