Triple
T14835719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salvador Johan Pérez Diaz |
E348824
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Salvador |
E44401
|
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: Salvador | Statement: [Salvador Johan Pérez Diaz, givenName, Salvador]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salvador Context triple: [Salvador Johan Pérez Diaz, givenName, Salvador]
-
A.
Salvador
Salvador is a metro station on Line 1 of the Santiago Metro in Santiago, Chile.
-
B.
Salvador
chosen
Salvador is the given name of the renowned Spanish surrealist artist Salvador Dalí.
-
C.
Salvador
"Salvador" is a 1986 political drama film directed by Oliver Stone, in which James Woods delivers an acclaimed performance as a cynical journalist covering the Salvadoran Civil War.
-
D.
Salvador
Salvador is a historic coastal city in northeastern Brazil known for its Afro-Brazilian culture, colonial architecture, and vibrant Carnival celebrations.
-
E.
São Salvador do Mundo
São Salvador do Mundo is a municipality on Santiago Island in Cape Verde, known for its rural communities and mountainous inland landscapes.
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded076ac9c8190a05cabec5e87d207 |
completed | April 14, 2026, 11:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe38a5d8888190821988ad00351d05 |
completed | May 8, 2026, 7:25 p.m. |
Created at: April 10, 2026, 1:52 a.m.