Triple
T215463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Red Sandstone |
E4810
|
entity |
| Predicate | thickness |
P9690
|
FINISHED |
| Object | several kilometres in some basins |
—
|
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: several kilometres in some basins | Statement: [Old Red Sandstone, thickness, several kilometres in some basins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thickness Context triple: [Old Red Sandstone, thickness, several kilometres in some basins]
-
A.
width
Indicates the measurement of how wide an entity is, typically the extent of its horizontal dimension from side to side.
-
B.
depth
Indicates the vertical distance from a reference surface or top point down to a lower point or bottom within a medium or space.
-
C.
weight
Indicates a relationship where a numerical value quantifies how heavy an entity is, often used to measure or compare mass or load.
-
D.
length
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
E.
skinThickness
Indicates the measured thickness of an entity’s skin, typically quantifying how thick its outer tissue layer is.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b52190481908f299d26122bafd2 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25dcba5148190ab80fd14c7cf4bb4 |
completed | Feb. 28, 2026, 3:15 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.