Triple
T952812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tallinn |
E20558
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | Kesklinn |
E28609
|
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: Kesklinn | Statement: [Tallinn, hasDistrict, Kesklinn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kesklinn Context triple: [Tallinn, hasDistrict, Kesklinn]
-
A.
Middelharnis
Middelharnis is a town in the western Netherlands known historically as a fishing and agricultural community on the island of Goeree-Overflakkee.
-
B.
Kezlev
Kezlev is the historical Crimean Tatar name for the city now known as Eupatoria, a coastal town on the western shore of Crimea.
-
C.
Karinska
Karinska was a renowned 20th-century costume designer best known for her influential work in ballet and theater, particularly with the New York City Ballet.
-
D.
Mitte
chosen
Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
-
E.
Nischel
Nischel is the local colloquial nickname for the large Karl Marx Monument in Chemnitz, Germany.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3d8f2e0819097554a301f8aa70f |
completed | March 1, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac119fd16c81908c43b6d3dc6d53b6 |
completed | March 7, 2026, 11:53 a.m. |
Created at: March 1, 2026, 7:40 p.m.