Triple
T7364313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atlantic passive margin |
E169827
|
entity |
| Predicate | typicalShelfWidth |
P19786
|
FINISHED |
| Object | hundreds of kilometers in places |
—
|
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: hundreds of kilometers in places | Statement: [Atlantic passive margin, typicalShelfWidth, hundreds of kilometers in places]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalShelfWidth Context triple: [Atlantic passive margin, typicalShelfWidth, hundreds of kilometers in places]
-
A.
typicalDimension
Indicates that one entity represents a standard or characteristic measurement (such as size, length, or capacity) typically associated with another entity.
-
B.
typicalWidth
chosen
Indicates the usual or characteristic width associated with an entity, as opposed to an exact or measured width in a specific instance.
-
C.
typicalStorePlacement
Indicates the usual or standard physical location where an item is placed or displayed within a store.
-
D.
typicalPanelSize
Indicates the usual or standard dimensions associated with a given panel.
-
E.
typicalHeight
Indicates the usual or characteristic height associated with an entity, such as a person, object, or species.
- 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_69c68a5ade988190885b7175f63b7534 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f26d6d6081909c7272a9ccae0d97 |
completed | March 27, 2026, 9:11 p.m. |
| PD | Predicate disambiguation | batch_69c6f02d36108190bcb34a95e6a30bd7 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:06 p.m.