Triple
T14190594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julio Daniel Martinez |
E351702
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Julio |
E118356
|
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: Julio | Statement: [Julio Daniel Martinez, givenName, Julio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julio Context triple: [Julio Daniel Martinez, givenName, Julio]
-
A.
Julio
chosen
Julio is the given name of Julio Antonio Mella, a prominent early 20th-century Cuban communist leader and co-founder of the Cuban Communist Party.
-
B.
Julio
Julio is a central character in the Spanish mystery drama series "High Seas," which follows intrigue and secrets aboard a luxury transatlantic ocean liner in the 1940s.
-
C.
Julián
Julián is a given name of Latin origin, commonly used in Spanish-speaking countries as a variant of Julian.
-
D.
Juano
Juano is the given name of Juano Hernández, a pioneering Afro-Puerto Rican actor known for his influential roles in mid-20th-century American cinema.
-
E.
Ambrosio
Ambrosio is the devout yet ultimately corrupt and tragic monastic protagonist of Matthew Gregory Lewis’s Gothic novel "The Monk."
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61df628c8190ba3f557e2128dce5 |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd1946eb68819096adf3c16a39818d |
completed | May 7, 2026, 10:59 p.m. |
Created at: April 10, 2026, 1:03 a.m.