Triple
T3156866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tatra Mountains |
E66004
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Morskie Oko |
E68467
|
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: Morskie Oko | Statement: [Tatra Mountains, contains, Morskie Oko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Morskie Oko Context triple: [Tatra Mountains, contains, Morskie Oko]
-
A.
Morskie Oko
chosen
Morskie Oko is a famous glacial lake in the Tatra Mountains of southern Poland, renowned for its scenic alpine setting and popularity as a hiking destination.
-
B.
Perekop Bay
Perekop Bay is a shallow inlet of the Black Sea located along the northern coast of Crimea, near the Isthmus of Perekop.
-
C.
Zalewo
Zalewo is a small town in northern Poland, situated in the Warmian-Masurian Voivodeship known for its lakes and natural landscapes.
-
D.
Kraljevica
Kraljevica is a coastal town in western Croatia known for its historic castles and shipyard on the Adriatic Sea.
-
E.
Savo
Savo is a town in Kenya’s Central Province known as one of the region’s notable settlements.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5eafa4c8190a65cc1312823144c |
completed | March 8, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b225068444819080e2b8b6b1260613 |
completed | March 12, 2026, 2:29 a.m. |
Created at: March 8, 2026, 3:05 p.m.