Triple
T5696308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suiko Seamount |
E125547
|
entity |
| Predicate | hasCrustalContext |
P66011
|
FINISHED |
| Object | oceanic crust |
—
|
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: oceanic crust | Statement: [Suiko Seamount, hasCrustalContext, oceanic crust]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCrustalContext Context triple: [Suiko Seamount, hasCrustalContext, oceanic crust]
-
A.
hasCrustalThickness
Indicates the relationship in which an object or region possesses a specified thickness of its crust.
-
B.
containsCraton
Indicates that one geological entity includes or encompasses a craton within its extent or structure.
-
C.
crustType
Indicates the specific style or form of crust associated with an item, such as a pizza or baked good.
-
D.
crustalComposition
Indicates the type and proportion of materials that make up a planet or moon’s outer solid layer (its crust).
-
E.
hasCrustType
Indicates that an entity (such as a pizza or pie) is associated with a specific type or style of crust.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c028fec2bc819083f5dca6a8d9d435 |
completed | March 22, 2026, 5:38 p.m. |
Created at: March 22, 2026, 3:45 p.m.