Triple
T26743010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweden and Finland |
E674316
|
entity |
| Predicate | hasPolicySimilarity |
P94757
|
FINISHED |
| Object | strong social safety nets |
—
|
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: strong social safety nets | Statement: [Sweden and Finland, hasPolicySimilarity, strong social safety nets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPolicySimilarity Context triple: [Sweden and Finland, hasPolicySimilarity, strong social safety nets]
-
A.
hasSimilarityTo
chosen
Indicates that one entity shares common characteristics, features, or qualities with another entity to a notable degree.
-
B.
hasRightsSimilarTo
Indicates that one entity possesses rights that are comparable or equivalent in scope or nature to those held by another entity.
-
C.
hasLexicalSimilarityWith
Indicates that two linguistic items share a significant degree of similarity in form, structure, or wording.
-
D.
hasPolicyReputationFor
Indicates that an entity is recognized or regarded in a particular way with respect to its policies or policy-related behavior.
-
E.
hasLetterSetSimilarity
Indicates that two entities share a similar set of letters, typically based on overlap or resemblance between the characters in their textual representations.
- 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_69eecda63a3881908095c47900692e65 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f757898fe48190b124dc7301672623 |
completed | May 3, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69f754c484348190948d2a04ff228fb1 |
completed | May 3, 2026, 1:59 p.m. |
Created at: April 27, 2026, 3:50 a.m.