Triple
T5925134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ikeja |
E131789
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | GRA Ikeja |
E131789
|
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: GRA Ikeja | Statement: [Ikeja, hasDistrict, GRA Ikeja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GRA Ikeja Context triple: [Ikeja, hasDistrict, GRA Ikeja]
-
A.
Ikeja
chosen
Ikeja is a major commercial and administrative hub in Nigeria, serving as the capital of Lagos State and hosting numerous businesses, government offices, and the Murtala Muhammed International Airport.
-
B.
Kōtō
Kōtō is a special ward in eastern Tokyo, Japan, known for its mix of residential neighborhoods, waterfront areas, and commercial districts.
-
C.
Ikeji Ile
Ikeji Ile is a prominent town in the Ijesha region of Osun State in southwestern Nigeria, known for its cultural and historical significance.
-
D.
Kasumigaseki, Tokyo
Kasumigaseki, Tokyo is a central government district in Chiyoda, Tokyo, known for housing numerous Japanese ministries, agencies, and administrative offices.
-
E.
Tachikawa
Tachikawa is a major city in western Tokyo, Japan, known as a key commercial and transportation hub of the Tama 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_69c0085b75e88190a632f9691f9da48b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03852806c81908ba726c16adf3358 |
completed | March 22, 2026, 6:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c04e5af8819095f15cfbc1f13c46 |
completed | March 23, 2026, 4:23 a.m. |
Created at: March 22, 2026, 4 p.m.