Triple
T12818559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USGS Clark Mountain |
E306466
|
entity |
| Predicate | hasContourLines |
P106520
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [USGS Clark Mountain, hasContourLines, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasContourLines Context triple: [USGS Clark Mountain, hasContourLines, true]
-
A.
hasContourInterval
Indicates that one entity specifies the vertical distance between contour lines represented by another entity.
-
B.
hasStraightLines
Indicates that the related entity possesses or is characterized by straight, non-curved lines.
-
C.
hasTopographicContext
Indicates that one entity is related to or characterized by a particular topographic or physical landscape context.
-
D.
hasLandscapeFeatures
Indicates that an entity possesses or includes specific landscape-related characteristics or elements.
-
E.
mapColorLines
Indicates a relationship where colors are assigned or associated with specific lines, such as mapping each line to a particular color.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e9d00088190ac0f5d60e1de7a7c |
completed | April 10, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69d964100f7481909a197396003d4a71 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d88be0481908c311f1e71b61e70 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:31 p.m.