Triple
T14297545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Mountains (Alaska) |
E354479
|
entity |
| Predicate | terrainShape |
P101500
|
FINISHED |
| Object | rounded mountains |
—
|
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: rounded mountains | Statement: [White Mountains (Alaska), terrainShape, rounded mountains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: terrainShape Context triple: [White Mountains (Alaska), terrainShape, rounded mountains]
-
A.
terrainFeature
Indicates a relationship where one entity is a natural or constructed landform or surface characteristic associated with a given location or area.
-
B.
terrainIncludes
Indicates that a specified terrain area contains or encompasses another geographic or environmental feature within its boundaries.
-
C.
terrainSpecialization
Indicates a relationship where an entity is particularly adapted or optimized for operation, performance, or use within a specific type of terrain.
-
D.
terrainStyle
chosen
Indicates the characteristic type or pattern of terrain associated with an entity or location.
-
E.
involvesTerrain
Indicates that the relationship or action takes place in, across, or is directly affected by a specified type of terrain or landform.
- F. None of above.
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_69d8278e17088190b328c5a9d4be74ff |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de717cfc948190ace5f1c91283b1c3 |
completed | April 14, 2026, 4:55 p.m. |
| PD | Predicate disambiguation | batch_69de2a8f81f08190af737e1654847aa6 |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:11 a.m.