Triple
T1268898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lagos (Portugal) |
E15664
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Lagos Marina |
E15664
|
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: Lagos Marina | Statement: [Lagos (Portugal), hasLandmark, Lagos Marina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lagos Marina Context triple: [Lagos (Portugal), hasLandmark, Lagos Marina]
-
A.
Apapa Port
Apapa Port is Nigeria’s largest and busiest seaport complex, serving as a major gateway for the country’s international maritime trade in Lagos.
-
B.
Lagos Island
Lagos Island is the historic and commercial heart of Lagos, Nigeria, housing major markets, financial institutions, and key government and cultural landmarks.
-
C.
Yenagoa
Yenagoa is the capital city of Bayelsa State in southern Nigeria, located in the oil-rich Niger Delta region.
-
D.
Port Harcourt
Port Harcourt is a major oil and industrial city in southern Nigeria and the capital of Rivers State.
-
E.
Lagos
chosen
Lagos is a historic coastal city in Portugal’s Algarve region, known for its scenic beaches, dramatic cliffs, and well-preserved old town.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c03aaa8c8190bacb7de5a38329da |
completed | March 1, 2026, 10:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac99898bdc8190bbbe28083b4a548b |
completed | March 7, 2026, 9:32 p.m. |
Created at: March 1, 2026, 7:50 p.m.