Triple
T5100223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OCB |
E114963
|
entity |
| Predicate | hasService |
P182
|
FINISHED |
| Object | TV Martí |
E114961
|
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: TV Martí | Statement: [OCB, hasService, TV Martí]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TV Martí Context triple: [OCB, hasService, TV Martí]
-
A.
TV Martí
chosen
TV Martí is a U.S. government-funded television service that broadcasts news and information to audiences in Cuba, aiming to promote freedom of information and democracy.
-
B.
Cayetano
Cayetano is a Spanish given name and surname, historically associated with Saint Cajetan and commonly used in Spanish-speaking countries.
-
C.
Totó
Totó is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
-
D.
Totó
Totó is a young boy who serves as one of the central child protagonists in Gabriel García Márquez’s short story "Light Is Like Water."
-
E.
Paco Yunque
Paco Yunque is a short story by Peruvian writer César Vallejo that portrays social injustice and class oppression through the experiences of a poor schoolboy.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd758381dc8190ac491788d27ab8e0 |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfc467008190ae704139f21edae2 |
completed | March 21, 2026, 5:05 p.m. |
Created at: March 20, 2026, 1:40 p.m.