Triple
T134281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SU |
E2716
|
entity |
| Predicate | relatedTopLevelDomain |
P248
|
FINISHED |
| Object | .su |
—
|
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: .su | Statement: [SU, relatedTopLevelDomain, .su]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedTopLevelDomain Context triple: [SU, relatedTopLevelDomain, .su]
-
A.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
B.
internetTLD
chosen
Indicates that one entity is the internet top-level domain (TLD) associated with the other entity.
-
C.
relatedCountry
Indicates that there is a relevant or associated relationship between an entity and a specified country, without specifying the exact nature of that relationship.
-
D.
relatedPlace
Indicates a relationship where one place is connected or associated with another place in a relevant or meaningful way.
-
E.
associatedCountry
Indicates that there is a relevant connection or linkage between an entity and a specific country, such as origin, operation, or affiliation.
- 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_69a2520c0f3481908b0ed054a2fca8d0 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a25788b2688190a45b39447f702551 |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a25650251c81908a6ea6368cd61198 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:30 a.m.