Triple
T18868403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mayor of Keda |
E461501
|
entity |
| Predicate | appliesToJurisdiction |
P82
|
FINISHED |
| Object | Keda |
—
|
NE NERFINISHED |
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: Keda | Statement: [Mayor of Keda, appliesToJurisdiction, Keda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keda Context triple: [Mayor of Keda, appliesToJurisdiction, Keda]
-
A.
Keda
chosen
Keda is a small town and administrative center in the mountainous Adjara region of southwestern Georgia.
-
B.
Kankia
Kankia is a town and local government area in northern Nigeria, known for its role as an administrative and commercial center within Katsina State.
-
C.
Kohly
Kohly is a residential neighborhood in the Playa municipality of Havana, Cuba, known for its quiet streets and proximity to major city avenues.
-
D.
Kaa
Kaa is a giant, hypnotic python who serves as a dangerous and manipulative predator in Disney’s live-action adaptation of The Jungle Book.
-
E.
Käina
Käina is a small settlement on the Estonian island of Hiiumaa, known for its coastal landscapes and traditional rural character.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c2a6d1d081909b6dab2a5166a317 |
completed | April 20, 2026, 6:07 a.m. |
Created at: April 10, 2026, 11:57 a.m.