Triple
T4218629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sal |
E94282
|
entity |
| Predicate | hasPort |
P35
|
FINISHED |
| Object | Palmeira |
E420994
|
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: Palmeira | Statement: [Sal, hasPort, Palmeira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Palmeira Context triple: [Sal, hasPort, Palmeira]
-
A.
Palmeira
chosen
Palmeira is a coastal town on the island of Sal in Cape Verde, known for its fishing harbor and role as a local transport and trade hub.
-
B.
Palmeira dos Índios
Palmeira dos Índios is a municipality in the Brazilian state of Alagoas, known for its cultural heritage and historical association with writer and politician Graciliano Ramos.
-
C.
Nipomo
Nipomo is a small unincorporated community in California’s Central Coast region, known for its agricultural roots and proximity to the Pacific Ocean in southern San Luis Obispo County.
-
D.
Itaquaquecetuba
Itaquaquecetuba is a municipality in the Greater São Paulo metropolitan area of southeastern Brazil, known for its rapid urban growth and industrial activity.
-
E.
Limeira
Limeira is a municipality in the interior of the Brazilian state of São Paulo, known for its industrial activity and history in the jewelry and citrus sectors.
- 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_69b3451997e08190851db4a9a588837d |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e098da881909a0cc339cc186627 |
completed | March 12, 2026, 11:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a858a5b08190990a896265ac9725 |
completed | March 14, 2026, 6:26 p.m. |
Created at: March 12, 2026, 11:04 p.m.