Triple
T23060653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kulon language |
E574290
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Kuron |
—
|
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: Kuron | Statement: [Kulon language, hasAlternativeName, Kuron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kuron Context triple: [Kulon language, hasAlternativeName, Kuron]
-
A.
Kuron
chosen
Kuron is an alternative name for the Kulon language, a lesser-known Austronesian language of Indonesia.
-
B.
Kullervo
Kullervo is a male given name of Finnish origin, notably borne by the politician Kullervo Manner and a tragic hero in the Finnish national epic Kalevala.
-
C.
Valday
Valday is a historic town in Russia’s Novgorod Oblast, known for its scenic lakes and location along major transport routes between Moscow and St. Petersburg.
-
D.
Eteläranta
Eteläranta is a central waterfront street and area in Helsinki, Finland, known for its proximity to the South Harbour and key government and commercial buildings.
-
E.
Liausson
Liausson is a small commune in southern France’s Hérault department, known for its scenic setting on the shores of the artificial Lac du Salagou.
- 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_69e245ba7ae48190be606dbc54120e39 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1899f359081909e89e19db3833a3d |
completed | April 29, 2026, 4:31 a.m. |
Created at: April 17, 2026, 3:55 p.m.