Triple
T31042700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurentian craton |
E791038
|
entity |
| Predicate | lithosphereThickness |
P74321
|
FINISHED |
| Object | up to about 200–250 km |
—
|
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: up to about 200–250 km | Statement: [Laurentian craton, lithosphereThickness, up to about 200–250 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lithosphereThickness Context triple: [Laurentian craton, lithosphereThickness, up to about 200–250 km]
-
A.
lithosphericThickness
chosen
Indicates the measured or modeled thickness of a planet’s lithosphere at a given location or region.
-
B.
lithosphereType
Indicates the classification of a lithospheric unit according to its type (e.g., oceanic, continental, or other defined categories).
-
C.
hasCrustalThickness
Indicates the relationship in which an object or region possesses a specified thickness of its crust.
-
D.
maximumBasaltThickness
Indicates the greatest measured or estimated thickness of basalt present in a given context or location.
-
E.
crustalComposition
Indicates the type and proportion of materials that make up a planet or moon’s outer solid layer (its crust).
- 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_69f224ca2fa881908a3ac5fedf207b90 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f694fb85f881909734cf13cea84c78 |
completed | May 3, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69f690f13d7481908ddfefe95df2a1c2 |
completed | May 3, 2026, 12:04 a.m. |
Created at: April 29, 2026, 8:59 p.m.