Triple
T18240976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Kreuznach |
E436807
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Giresun |
—
|
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: Giresun | Statement: [Bad Kreuznach, twinnedWith, Giresun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Giresun Context triple: [Bad Kreuznach, twinnedWith, Giresun]
-
A.
Giresun
chosen
Giresun is a coastal city in northeastern Turkey known for its hazelnut production and scenic location along the Black Sea.
-
B.
Kastamonu
Kastamonu is a historic city in northern Turkey known for its well-preserved Ottoman architecture and role as the administrative center of Kastamonu Province.
-
C.
Kırklareli
Kırklareli is a city in northwestern Turkey known for its location near the Bulgarian border, agricultural economy, and historical Ottoman-era architecture.
-
D.
Giresun Province
Giresun Province is a coastal province in northeastern Turkey along the Black Sea, known for its lush green landscapes and extensive hazelnut production.
-
E.
Trabzon
Trabzon is a historic city in northeastern Turkey that serves as a major Black Sea port and regional cultural and commercial center.
- 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e287548190b666a990e5b168b0 |
completed | April 19, 2026, 3:42 p.m. |
Created at: April 10, 2026, 10:33 a.m.