Triple
T13993246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vivir |
E336632
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | María Isabel |
E396627
|
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: María Isabel | Statement: [Vivir, hasTrack, María Isabel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: María Isabel Context triple: [Vivir, hasTrack, María Isabel]
-
A.
María Isabel
chosen
María Isabel is the birth name of Spanish actress Maribel Verdú, known for her prominent roles in films such as "Y Tu Mamá También" and "Pan's Labyrinth."
-
B.
María Isabel
María Isabel, better known as Chábeli Iglesias, is a Spanish journalist and television personality from the prominent Iglesias entertainment family.
-
C.
María Teresa
María Teresa is the Cuban-born Grand Duchess of Luxembourg, known for her humanitarian work and role as the consort of Grand Duke Henri.
-
D.
María de la Paz
María de la Paz is the full given name of Spanish actress Paz Vega, known for her work in both Spanish and international cinema.
-
E.
María de las Mercedes
María de las Mercedes was a 19th-century Spanish queen consort, best known as the first wife of King Alfonso XII of Spain.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb3b5d881909f15a1e08bb202f3 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff677935408190a28af4cd34d82aa4 |
completed | May 9, 2026, 4:57 p.m. |
Created at: April 9, 2026, 10:19 p.m.