Triple
T8744519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Checkerboard Mesa |
E207789
|
entity |
| Predicate | hasSurfacePattern |
P60649
|
FINISHED |
| Object | erosion-induced grid of cracks and grooves |
—
|
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: erosion-induced grid of cracks and grooves | Statement: [Checkerboard Mesa, hasSurfacePattern, erosion-induced grid of cracks and grooves]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurfacePattern Context triple: [Checkerboard Mesa, hasSurfacePattern, erosion-induced grid of cracks and grooves]
-
A.
hasSurfaceQuality
chosen
Indicates that one entity possesses a particular characteristic or condition of its surface.
-
B.
hasPavementPattern
Indicates that an entity possesses or is characterized by a specific pattern or design in its pavement surface.
-
C.
hasAlternativeSurface
Indicates that one entity serves as a different or substitute surface option for another entity.
-
D.
hasSurfaceComposition
Indicates that one entity has a surface made up of, or characterized by, the material or composition specified by another entity.
-
E.
hasSurfaceSections
Indicates that an entity is composed of or divided into distinct sections or parts of its surface.
- 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_69ca835bb2bc819084bb5906cb6ef7f8 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d72e47c819099540d062d35ebd5 |
completed | March 31, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69cc5c160dac8190b4aeb4bf0529de52 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:38 p.m.