Triple
T12992223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Охта |
E321932
|
entity |
| Predicate | continent |
P233
|
FINISHED |
| Object | Европа |
E925900
|
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: Европа | Statement: [Охта, continent, Европа]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Европа Context triple: [Охта, continent, Европа]
-
A.
Ευρώπη
chosen
Η Ευρώπη είναι μία από τις ηπείρους της Γης, γνωστή για την πλούσια ιστορία, τον πολιτισμό της και τον σημαντικό ρόλο της στην παγκόσμια πολιτική και οικονομία.
-
B.
Europa
Europa is a figure in Greek mythology, a Phoenician princess famously abducted by Zeus and later the eponymous queen of Crete.
-
C.
Europa
Europa is the primary continent-spanning, pseudo-European steampunk world in the Girl Genius webcomic, filled with mad science, clanking constructs, and warring powers.
-
D.
Europa
Europa is a 1991 surreal, noir-style drama film by Danish director Lars von Trier, known for its striking visual style and hypnotic narrative set in post-World War II Germany.
-
E.
Europa
Europa is one of Jupiter’s large icy moons, notable for its smooth frozen surface and the subsurface ocean that makes it a prime candidate in the search for extraterrestrial life.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7765788190a9503ef055bc30ca |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbc277c881909ae77e8a44e06986 |
completed | May 3, 2026, 4:14 a.m. |
Created at: April 9, 2026, 8:44 p.m.