Triple
T658876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastern Nigeria |
E11709
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Port Harcourt |
E12365
|
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: Port Harcourt | Statement: [Eastern Nigeria, containsCity, Port Harcourt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Port Harcourt Context triple: [Eastern Nigeria, containsCity, Port Harcourt]
-
A.
Port Harcourt
chosen
Port Harcourt is a major oil and industrial city in southern Nigeria and the capital of Rivers State.
-
B.
Lagos
Lagos is a major coastal megacity in southwestern Nigeria, known as the country’s economic hub and one of Africa’s most populous and vibrant urban centers.
-
C.
Lagos
Lagos is a historic coastal city in Portugal’s Algarve region, known for its scenic beaches, dramatic cliffs, and well-preserved old town.
-
D.
Bauchi
Bauchi is a prominent city in northeastern Nigeria that serves as the capital of Bauchi State and a key commercial and administrative center in the region.
-
E.
Yenagoa
Yenagoa is the capital city of Bayelsa State in southern Nigeria, located in the oil-rich Niger Delta region.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49fa705c48190a29952c1f7cab901 |
completed | March 1, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a654d435088190b4a910dc280d13c6 |
completed | March 3, 2026, 3:26 a.m. |
Created at: March 1, 2026, 7:36 p.m.