Triple
T3533990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomé de Sousa |
E74725
|
entity |
| Predicate | founded |
P104
|
FINISHED |
| Object | Salvador |
E62572
|
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: [Tomé de Sousa, founded, Salvador]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salvador Context triple: [Tomé de Sousa, founded, Salvador]
-
A.
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.
-
B.
Salvador
Salvador is a metro station on Line 1 of the Santiago Metro in Santiago, Chile.
-
C.
Salvador
Salvador is the given name of the renowned Spanish surrealist artist Salvador Dalí.
-
D.
Port of Salvador
The Port of Salvador is a major Brazilian seaport and cargo hub on the Atlantic coast, serving as a key gateway for trade in northeastern Brazil.
-
E.
Salvador, Bahia, Brazil
chosen
Salvador, the capital of Brazil’s Bahia state, is a major coastal city known for its Afro-Brazilian culture, colonial architecture, and historic role as the country’s first capital.
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc9cff5c81909011f34c9bf28e11 |
completed | March 8, 2026, 6:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e9c408481909a808d400f545ff8 |
completed | March 13, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:19 p.m.