Triple
T12753805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dique do Tororó |
E304804
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bahia |
E199233
|
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: Bahia | Statement: [Dique do Tororó, locatedIn, Bahia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bahia Context triple: [Dique do Tororó, locatedIn, Bahia]
-
A.
Bahia
Bahia is a traditional Brazilian football club based in Salvador, known for its passionate fanbase and historic success in national competitions.
-
B.
Bahia
chosen
Bahia is a large and culturally rich state in northeastern Brazil, known for its Afro-Brazilian heritage, historic city of Salvador, and extensive Atlantic coastline.
-
C.
Portuguesa State
Portuguesa State is a landlocked agricultural region in western Venezuela known for its extensive plains and significant crop production, particularly of rice and corn.
-
D.
Alagoas
Alagoas is a small coastal state in northeastern Brazil known for its picturesque beaches, lagoons, and colonial-era history.
-
E.
Maranhão
Maranhão is a northeastern Brazilian state known for its colonial heritage, Afro-Brazilian culture, and the Lençóis Maranhenses dune and lagoon 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f739688734819092b14bd3edacd4ef |
completed | May 3, 2026, 12:02 p.m. |
Created at: April 9, 2026, 5:27 p.m.