Triple
T12271233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2867 Šteins |
E292473
|
entity |
| Predicate | hasShapeModel |
P104162
|
FINISHED |
| Object | derived from Rosetta imaging |
—
|
LITERAL 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: derived from Rosetta imaging | Statement: [2867 Šteins, hasShapeModel, derived from Rosetta imaging]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShapeModel Context triple: [2867 Šteins, hasShapeModel, derived from Rosetta imaging]
-
A.
hasSilhouetteShape
Indicates that one entity has the overall outline or contour shape specified or characterized by another entity.
-
B.
hasHemShape
Indicates that an item possesses a specific form or contour of its hem or lower edge.
-
C.
hasApproximateShape
Indicates that one entity has a shape that is similar to, but not exactly the same as, the shape of another entity.
-
D.
hasRealModel
Indicates that an abstract, theoretical, or simplified entity is associated with a corresponding concrete or physically instantiated model in the real world.
-
E.
hasTerminalShape
Indicates that one entity possesses or exhibits a particular terminal (end) shape defined by another entity.
- F. None of above. chosen
Provenance (4 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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9380a5e78819086bd4dfe9a83d1f5 |
completed | April 10, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69d91c4a66cc819083ce6fcaf5042af6 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d93805cee08190a532ebcf5908e617 |
completed | April 10, 2026, 5:48 p.m. |
Created at: April 8, 2026, 9:52 p.m.