Triple
T4214639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JUICE mission |
E94185
|
entity |
| Predicate | target |
P860
|
FINISHED |
| Object | Europa |
E126262
|
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: Europa | Statement: [JUICE mission, target, Europa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Europa Context triple: [JUICE mission, target, Europa]
-
A.
Europa
chosen
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.
-
B.
Europa
Europa is a European-themed section of the Worlds of Fun amusement park in Kansas City, Missouri, featuring attractions, architecture, and cuisine inspired by various European countries.
-
C.
Europa
Europa is a figure in Greek mythology, a Phoenician princess famously abducted by Zeus and later the eponymous queen of Crete.
-
D.
Europe
Europe is a diverse continent in the Northern Hemisphere known for its rich history, cultural heritage, and significant influence on global politics, economics, and science.
-
E.
Eurasia
Eurasia is the vast combined continental landmass of Europe and Asia, forming the largest continuous land area on Earth.
- 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_69b3451997e08190851db4a9a588837d |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34be8ba408190baee362e5abbe75b |
completed | March 12, 2026, 11:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a852e0448190bc488087e92a94d4 |
completed | March 14, 2026, 6:26 p.m. |
Created at: March 12, 2026, 11:04 p.m.