Triple
T5522602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Ustka |
E144845
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Slupsk |
E178809
|
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: Slupsk | Statement: [Port of Ustka, hasNearbyCity, Slupsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Slupsk Context triple: [Port of Ustka, hasNearbyCity, Slupsk]
-
A.
Słupsk
chosen
Słupsk is a historic city in northern Poland known for its medieval architecture and location near the Baltic Sea.
-
B.
Elbląg
Elbląg is a historic city in northern Poland known for its reconstructed Old Town, medieval heritage, and role as an important port and industrial center.
-
C.
Sopot
Sopot is a Polish Baltic Sea resort city famous for its sandy beaches, long wooden pier, and vibrant spa and nightlife culture.
-
D.
Sopot
Sopot is a suburban municipality of Belgrade, Serbia, known for its rural character and proximity to the Avala and Kosmaj mountains.
-
E.
Kwidzyn
Kwidzyn is a historic town in northern Poland known for its medieval Teutonic castle complex and Gothic cathedral.
- 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_69c008f873a481909b4d9f7e2db3c37d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f73cc8c8190a92a839c1ca804c7 |
completed | March 22, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027f2e98c8190880752c9ae8aba4f |
completed | March 22, 2026, 5:33 p.m. |
Created at: March 22, 2026, 3:34 p.m.