Triple
T12643832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oguta Lake |
E301967
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Oguta |
E368591
|
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: Oguta | Statement: [Oguta Lake, locatedIn, Oguta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oguta Context triple: [Oguta Lake, locatedIn, Oguta]
-
A.
Oguta
chosen
Oguta is a town and local government area in southeastern Nigeria known for its scenic Oguta Lake and cultural significance within Imo State.
-
B.
Orito
Orito is a municipality and town located in the Putumayo Department of southwestern Colombia, known for its role in regional oil production and its position within the Amazonian foothills.
-
C.
Nakawa
Nakawa is one of the energetic human hosts in Disney’s “Festival of the Lion King” stage show at Disney’s Animal Kingdom.
-
D.
Ogawa
Ogawa is a town in Saitama Prefecture, Japan, known for its traditional Japanese paper (washi) production and its role as a local transport hub.
-
E.
Izumiotsu
Izumiotsu is a coastal city in Osaka Prefecture, Japan, known for its port facilities and industrial waterfront along Osaka Bay.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9614bf2f881909976becdf747f4fb |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6fef8d94081908ea5ac426e22ef87 |
completed | May 3, 2026, 7:53 a.m. |
Created at: April 9, 2026, 5:17 p.m.