Triple
T7657819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helsingborg |
E173429
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Tartu |
E43129
|
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: Tartu | Statement: [Helsingborg, hasTwinTown, Tartu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tartu Context triple: [Helsingborg, hasTwinTown, Tartu]
-
A.
Tartu
chosen
Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
-
B.
Tallinn
Tallinn is the capital and largest city of Estonia, a historic Baltic Sea port known for its well-preserved medieval Old Town and strategic maritime location.
-
C.
Pärnu
Pärnu is a coastal city in southwestern Estonia known as a popular summer resort and spa destination on the Baltic Sea.
-
D.
Viljandi
Viljandi is a historic town in southern Estonia known for its medieval castle ruins, rich cultural life, and annual folk music festival.
-
E.
Maardu
Maardu is an industrial town in northern Estonia, located just east of the capital Tallinn in Harju County.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7019161548190855a5b1e9f5d7e99 |
completed | March 27, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8a21dd3088190bb026de65970a14b |
completed | March 29, 2026, 3:53 a.m. |
Created at: March 27, 2026, 3:59 p.m.