Triple
T16386463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes Ruehl |
E397934
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Mercedes
Mercedes is a feminine given name of Spanish origin, commonly associated with the Virgin Mary and used in various Spanish-speaking cultures.
|
E1089467
|
NE FINISHED |
How this triple was built (4 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 Ruehl, givenName, Mercedes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mercedes Context triple: [Mercedes Ruehl, 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 coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and fishing-based local economy.
-
C.
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.
-
D.
Mercedes
Mercedes is a coastal municipality in the Philippine province of Camarines Norte known for its fishing industry and nearby island attractions.
-
E.
Mercedes
Mercedes is a German Formula One team and automotive manufacturer renowned for its dominant performance in the early hybrid era of F1.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mercedes Triple: [Mercedes Ruehl, givenName, Mercedes]
Generated description
Mercedes is a feminine given name of Spanish origin, commonly associated with the Virgin Mary and used in various Spanish-speaking cultures.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mercedes Target entity description: Mercedes is a feminine given name of Spanish origin, commonly associated with the Virgin Mary and used in various Spanish-speaking cultures.
-
A.
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.
-
B.
Mercedes
Mercedes is the given first name of the British former ballerina and television personality Darcey Bussell.
-
C.
Mercedes
Mercedes is a coastal municipality in the Philippine province of Camarines Norte known for its fishing industry and nearby island attractions.
-
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
Mercedes is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and fishing-based local economy.
- F. None of above.
Provenance (5 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e3263d260081909db9ac6016d5738a |
completed | April 18, 2026, 6:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00356cf44081909133b599cfe9ed4a |
completed | May 10, 2026, 7:36 a.m. |
| NEDg | Description generation | batch_6a00369391a08190bb5521e2fcc839c6 |
completed | May 10, 2026, 7:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00374326948190ae039bc689054387 |
completed | May 10, 2026, 7:44 a.m. |
Created at: April 10, 2026, 5:08 a.m.