Triple
T20134817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes Tomasa de San Martín |
E490997
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mercedes |
—
|
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: Mercedes | Statement: [Mercedes Tomasa de San Martín, givenName, Mercedes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mercedes Context triple: [Mercedes Tomasa de San Martín, givenName, Mercedes]
-
A.
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."
-
B.
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.
-
C.
Mercedes
Mercedes is the given first name of the British former ballerina and television personality Darcey Bussell.
-
D.
Mercedes
Mercedes is a German Formula One team and automotive manufacturer renowned for its dominant performance in the early hybrid era of F1.
-
E.
Mercedes
chosen
Mercedes is a feminine given name of Spanish origin that became widely known through its association with the early automobile brand Mercedes-Benz.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66766e46c81908721fd47066dc9f8 |
completed | April 20, 2026, 5:50 p.m. |
Created at: April 11, 2026, 11:32 p.m.