Triple
T942799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pluto |
E20342
|
entity |
| Predicate | mountainComposition |
P22036
|
FINISHED |
| Object | water ice |
—
|
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: water ice | Statement: [Pluto, mountainComposition, water ice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mountainComposition Context triple: [Pluto, mountainComposition, water ice]
-
A.
mountainRangeType
Indicates the classification or category of a mountain range based on its geological or geographical type.
-
B.
mountainRange
Indicates that one entity is a mountain range that the other entity is part of, associated with, or located in.
-
C.
mountainSystem
Indicates a relationship where multiple mountains are grouped together as part of the same connected or coherent mountain system or range.
-
D.
sourceMountainRange
Indicates that a river, stream, or similar feature originates from or has its source in a specified mountain range.
-
E.
hasMountain
Indicates that a location or region possesses or contains at least one mountain.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3a1a4888190997adf56eb761431 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b29dc8dc8190b9d33f70f8563d61 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b344f6f48190ba03ce593c94176b |
completed | March 1, 2026, 9:44 p.m. |
Created at: March 1, 2026, 7:40 p.m.