Triple
T12294357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guarulhos |
E293044
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Santa Isabel |
E955151
|
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: Santa Isabel | Statement: [Guarulhos, borderedBy, Santa Isabel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Isabel Context triple: [Guarulhos, borderedBy, Santa Isabel]
-
A.
Santa Isabel
Santa Isabel was a Spanish expedition ship associated with the Santa Cruz colony during the era of New World exploration.
-
B.
Santa Isabel
Santa Isabel is a supermarket chain in Latin America operated under the retail group Cencosud.
-
C.
Santa Isabel
Santa Isabel is a coastal municipality in southern Puerto Rico known for its agricultural production, particularly sugarcane and plantains.
-
D.
Santa Isabel
Santa Isabel is a town in southern Ecuador’s Azuay Province, known as a local commercial and agricultural center in the region.
-
E.
Santa Isabel
chosen
Santa Isabel is a municipality in the state of São Paulo, Brazil, known for its preserved natural areas and role as part of the greater São Paulo region.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93ed7251c8190b94d7cd75ad49b9c |
completed | April 10, 2026, 6:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e79bf548190bf7f314222ed1ed1 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.