Triple
T16448702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stato da Mar |
E399497
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Cattaro |
E450140
|
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: Cattaro | Statement: [Stato da Mar, hasPart, Cattaro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cattaro Context triple: [Stato da Mar, hasPart, Cattaro]
-
A.
Cattaro
chosen
Cattaro is the historical Italian name for the coastal town of Kotor in Montenegro, known for its well-preserved medieval old town and bay.
-
B.
Maleva
Maleva is the wise Romani woman and mother of the original Wolf Man who serves as a mystical guide and bearer of the werewolf curse’s lore in Universal’s classic horror films.
-
C.
Emden
Emden is a historic port city in northwestern Germany known for its maritime industry and location near the North Sea.
-
D.
Kotlas
Kotlas is a significant industrial and transport hub in northern Russia, located at the confluence of the Vychegda and Northern Dvina rivers.
-
E.
Alushta
Alushta is a resort town on the southern coast of Crimea, known for its beaches, mild climate, and role as a popular Black Sea tourist destination.
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32cdee44c8190ae0df20c58ff7558 |
completed | April 18, 2026, 7:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004594a4508190be08f3acfff36ab0 |
completed | May 10, 2026, 8:45 a.m. |
Created at: April 10, 2026, 5:10 a.m.