Triple
T6599375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thalía |
E148556
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
María Mercedes
María Mercedes is a popular 1990s Mexican telenovela starring Thalía as a poor young woman whose life changes dramatically after an unexpected inheritance.
|
E607234
|
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: María Mercedes | Statement: [Thalía, notableWork, María Mercedes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: María Mercedes Context triple: [Thalía, notableWork, María Mercedes]
-
A.
María del Carmen
María del Carmen is a Spanish-language feminine given name commonly used in Hispanic cultures, often in honor of Our Lady of Mount Carmel.
-
B.
Maria Paola
Maria Paola is the Italian form of the given name borne by Pauline Bonaparte, Napoleon Bonaparte’s influential and famously beautiful sister.
-
C.
Margarita Isabel
Margarita Isabel was a Mexican actress known for her work in film, television, and theater.
-
D.
María del Rosario
María del Rosario is the given name of Cayetana Fitz-James Stuart, the 18th Duchess of Alba, a prominent Spanish aristocrat known for holding a record number of noble titles.
-
E.
María
"María" is a film featuring actress Taryn Power in a significant role.
- 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: María Mercedes Triple: [Thalía, notableWork, María Mercedes]
Generated description
María Mercedes is a popular 1990s Mexican telenovela starring Thalía as a poor young woman whose life changes dramatically after an unexpected inheritance.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: María Mercedes Target entity description: María Mercedes is a popular 1990s Mexican telenovela starring Thalía as a poor young woman whose life changes dramatically after an unexpected inheritance.
-
A.
María del Carmen
María del Carmen is a Spanish-language feminine given name commonly used in Hispanic cultures, often in honor of Our Lady of Mount Carmel.
-
B.
Maria Paola
Maria Paola is the Italian form of the given name borne by Pauline Bonaparte, Napoleon Bonaparte’s influential and famously beautiful sister.
-
C.
Margarita Isabel
Margarita Isabel was a Mexican actress known for her work in film, television, and theater.
-
D.
María del Rosario
María del Rosario is the given name of Cayetana Fitz-James Stuart, the 18th Duchess of Alba, a prominent Spanish aristocrat known for holding a record number of noble titles.
-
E.
María
"María" is a film featuring actress Taryn Power in a significant role.
- F. None of above. chosen
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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6aeeffdf0819090af7bba918bef84 |
completed | March 27, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e43224dc81909dea493a5ee2726e |
completed | March 27, 2026, 8:10 p.m. |
| NEDg | Description generation | batch_69c6e4e9c344819099ad11c21c2e4a6e |
completed | March 27, 2026, 8:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6e581a4f88190b1f64033a49d1bae |
completed | March 27, 2026, 8:16 p.m. |
Created at: March 27, 2026, 1:56 p.m.