Triple
T5974350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cassini Division |
E132947
|
entity |
| Predicate | orbitalResonanceWith |
P17319
|
FINISHED |
| Object | Mimas |
E148401
|
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: Mimas | Statement: [Cassini Division, orbitalResonanceWith, Mimas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mimas Context triple: [Cassini Division, orbitalResonanceWith, Mimas]
-
A.
Mimas
chosen
Mimas is a small, heavily cratered icy moon of Saturn best known for its large Herschel crater, which gives it a distinctive "Death Star"-like appearance.
-
B.
Mimas
Mimas is a giant from Greek mythology, often depicted as one of the monstrous offspring of Gaia who fought the Olympian gods in the Gigantomachy.
-
C.
Tethys
Tethys is one of Saturn’s mid-sized icy moons, known for its bright, heavily cratered surface and massive Odysseus impact basin.
-
D.
Tethys
Tethys is a Titaness in Greek mythology, traditionally regarded as a primordial sea goddess and wife of Oceanus.
-
E.
Dione
Dione is an icy mid-sized moon of Saturn known for its bright, heavily cratered surface and extensive system of fractures and cliffs.
- 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_69c0086deab081908550159ca23eec9b |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a39c9d08190a78e1e3268584eb5 |
completed | March 22, 2026, 7:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c108422220819092ac63e5ebb264b4 |
completed | March 23, 2026, 9:30 a.m. |
Created at: March 22, 2026, 4:03 p.m.