Triple
T18621722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sangerhausen |
E455166
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Kohtla-Järve |
—
|
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: Kohtla-Järve | Statement: [Sangerhausen, hasTwinTown, Kohtla-Järve]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kohtla-Järve Context triple: [Sangerhausen, hasTwinTown, Kohtla-Järve]
-
A.
Kohtla-Järve
chosen
Kohtla-Järve is an industrial city in northeastern Estonia known for its oil shale industry and diverse population.
-
B.
Jõgeva
Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
-
C.
Võru
Võru is a small town in southeastern Estonia known for its lakeside setting, traditional Võro culture, and role as a regional administrative and cultural center.
-
D.
Haapsalu
Haapsalu is a small seaside town in western Estonia known for its historic wooden architecture, medieval castle, and traditional seaside resort and spa culture.
-
E.
Tartu
Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
- 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54f0146f48190a872032db6e660c6 |
completed | April 19, 2026, 9:54 p.m. |
Created at: April 10, 2026, 11:46 a.m.