Triple
T13982274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Igala |
E336340
|
entity |
| Predicate | neighboringEthnicGroup |
P11274
|
FINISHED |
| Object | Ebira |
E328638
|
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: Ebira | Statement: [Igala, neighboringEthnicGroup, Ebira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ebira Context triple: [Igala, neighboringEthnicGroup, Ebira]
-
A.
Ebira
chosen
The Ebira are an ethnic group in central Nigeria known for their rich cultural heritage, distinctive language, and traditional festivals.
-
B.
Erba
Erba is a town in the Lombardy region of northern Italy, situated near Lake Como and known for its scenic surroundings and local industry.
-
C.
Ybanag
Ybanag is an Austronesian language spoken primarily in the Cagayan Valley region of the northern Philippines by the Ibanag people.
-
D.
Iribe
Iribe is the surname of Brendan Iribe, the American entrepreneur best known as a co-founder and former CEO of virtual reality company Oculus VR.
-
E.
Erieye
Erieye is an airborne early warning and control (AEW&C) radar system developed by Saab for long-range surveillance and airspace management.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ea2e8808190a1203a6386224bd8 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1e5f2008190a0701ae37ed5219d |
completed | May 6, 2026, 8:17 p.m. |
Created at: April 9, 2026, 10:18 p.m.