Triple
T34738392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frieder |
E1001417
|
entity |
| Predicate | hasNotableRegionAssociation |
P131006
|
FINISHED |
| Object | American sports |
—
|
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: American sports | Statement: [Frieder, hasNotableRegionAssociation, American sports]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableRegionAssociation Context triple: [Frieder, hasNotableRegionAssociation, American sports]
-
A.
hasNotableLocationAssociation
Indicates that there is a significant or noteworthy connection between an entity and a specific location.
-
B.
hasNotabilityRegion
chosen
Indicates that an entity’s notability, prominence, or recognition is specifically associated with a particular geographic region.
-
C.
hasNotableUsageRegion
Indicates that something is prominently or distinctively used within a particular geographic region.
-
D.
regionOfAssociation
Indicates a broader geographic or spatial area with which an entity is functionally, contextually, or organizationally associated.
-
E.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
- 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_69f76daf739881909ed3554f98a2b433 |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a0340784c488190aa6c7c9be2e8a434 |
completed | May 12, 2026, 3 p.m. |
| PD | Predicate disambiguation | batch_6a033f33eddc819091507716f6ed7d7d |
completed | May 12, 2026, 2:54 p.m. |
Created at: May 3, 2026, 3:59 p.m.