Triple
T12099412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sangotedo |
E288151
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Lekki Peninsula |
E288149
|
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: Lekki Peninsula | Statement: [Sangotedo, region, Lekki Peninsula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lekki Peninsula Context triple: [Sangotedo, region, Lekki Peninsula]
-
A.
Lekki
chosen
Lekki is a rapidly developing coastal city and affluent residential and commercial hub in Lagos State, Nigeria.
-
B.
Lekki
Lekki is the official mascot character created for the XVIII Olympic Winter Games.
-
C.
Lekki
Lekki is a fictional companion mascot character associated with Nokki, likely designed as a cute, supportive sidekick figure.
-
D.
Lekki Free Trade Zone
Lekki Free Trade Zone is a major industrial and commercial hub in Lagos State, Nigeria, designed to attract foreign investment and support large-scale manufacturing, logistics, and trade.
-
E.
Yaba
Yaba is a bustling commercial and residential district on Lagos Mainland in Nigeria, known for its markets, educational institutions, and growing tech startup scene.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9155465388190bbe52453c9b11912 |
completed | April 10, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68e9d94b48190b026d5a0ad3d6ce7 |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 8, 2026, 9:48 p.m.