Triple
T13098460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yakurr |
E310652
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Ikom |
E306871
|
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: Ikom | Statement: [Yakurr, hasSettlement, Ikom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ikom Context triple: [Yakurr, hasSettlement, Ikom]
-
A.
Ikom
chosen
Ikom is a prominent commercial and administrative town in southeastern Nigeria known for its cocoa production and strategic location near the Cameroon border.
-
B.
Oimachi
Oimachi is a commercial and residential district in Tokyo known for its busy train hub, shopping streets, and convenient access to central Shinagawa and other parts of the city.
-
C.
Kumiya
Kumiya is a Berber tribal group historically associated with the rise and support base of the Almohad leader Abd al-Mu’min in the Maghreb.
-
D.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
-
E.
Ikoma
Ikoma is a city in Japan known for its scenic setting on the slopes of Mount Ikoma and its role as a residential and commuter hub near Osaka and Nara.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d981500d34819097037b3c3c33627b |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d619b82c819093d0d98db88eb9ae |
completed | May 3, 2026, 4:59 a.m. |
Created at: April 9, 2026, 9:04 p.m.