Triple
T2712410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bight of Benin |
E59892
|
entity |
| Predicate | hasCoastalCity |
P969
|
FINISHED |
| Object | Porto-Novo |
E171111
|
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: Porto-Novo | Statement: [Bight of Benin, hasCoastalCity, Porto-Novo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Porto-Novo Context triple: [Bight of Benin, hasCoastalCity, Porto-Novo]
-
A.
Porto-Novo
chosen
Porto-Novo is the official capital city of Benin, known for its colonial architecture and role as a political and cultural center in West Africa.
-
B.
João Pessoa
João Pessoa is the capital and largest city of the Brazilian state of Paraíba, known for its historic colonial architecture and easternmost location in the Americas.
-
C.
Campina Grande
Campina Grande is a major city in northeastern Brazil known for its technology and education hubs and for hosting one of the world’s largest São João (June) festivals.
-
D.
San-Pédro
San-Pédro is a major port city in southwestern Côte d'Ivoire, known especially for its role in the export of cocoa and other goods.
-
E.
Maceió
Maceió is a coastal city in northeastern Brazil known for its white-sand beaches, turquoise waters, and vibrant tourism industry.
- 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_69ab4ac92a088190bc74bca14038e3de |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda90d5a88190bcb6caefd8f2dd1b |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afaf83e37c8190b7a7815f8acf3a4b |
completed | March 10, 2026, 5:43 a.m. |
Created at: March 6, 2026, 9:55 p.m.