Triple
T6776854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayuu language |
E155578
|
entity |
| Predicate | hasPossessionMarking |
P72730
|
FINISHED |
| Object | prefixes on nouns |
—
|
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: prefixes on nouns | Statement: [Wayuu language, hasPossessionMarking, prefixes on nouns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPossessionMarking Context triple: [Wayuu language, hasPossessionMarking, prefixes on nouns]
-
A.
hasFloorMarkings
Indicates that an entity features visible markings or lines on its floor surface, typically used for guidance, organization, or safety.
-
B.
hasRunwayMarkings
Indicates that a runway possesses specific painted markings or symbols on its surface.
-
C.
hasMeasurementMarkings
Indicates that one entity bears visible measurement indicators or scale markings on its surface for quantifying something.
-
D.
hasMarker
Indicates that one entity possesses, is associated with, or is identified by a specific marker.
-
E.
hasEndZonesColor
Indicates that the specified color is the one used for the end zones associated with an entity.
- 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_69c688162bf8819088b664b5c3b5be7a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d26725208190b64935cfd08b2aff |
completed | March 27, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69c6d095dcac8190bb9b943f50a7f885 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d182213c819086fcbbfd3d64d80b |
completed | March 27, 2026, 6:50 p.m. |
Created at: March 27, 2026, 2:13 p.m.