Triple
T10094867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Abra |
E215838
|
entity |
| Predicate | hasExtractionType |
P92403
|
FINISHED |
| Object | surface mining |
—
|
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: surface mining | Statement: [El Abra, hasExtractionType, surface mining]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExtractionType Context triple: [El Abra, hasExtractionType, surface mining]
-
A.
hasReconstructionType
Indicates the specific method or category of reconstruction applied to an object, structure, or dataset.
-
B.
hasAcquisitionType
Indicates the specific kind or category of acquisition relationship that exists between one entity acquiring another.
-
C.
hasExpansionType
Indicates that an entity is associated with a particular mode or category of expansion (such as how it grows, extends, or scales).
-
D.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
E.
hasDiscoveryType
Indicates the specific manner, method, or category by which something was discovered.
- 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_69ca83a4947c8190823a7495dc5d96ed |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd0784c288190967d143beca32c4b |
completed | April 2, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9b853c8190a2af993ce9b21309 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd5150ae98819086c4f822114b4e2c |
completed | April 1, 2026, 5:09 p.m. |
Created at: March 30, 2026, 9:02 p.m.