Triple
T4566682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baron |
E121925
|
entity |
| Predicate | equivalentOrSimilarTo |
P6530
|
FINISHED |
| Object | Freiherr in German-speaking countries |
—
|
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: Freiherr in German-speaking countries | Statement: [Baron, equivalentOrSimilarTo, Freiherr in German-speaking countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentOrSimilarTo Context triple: [Baron, equivalentOrSimilarTo, Freiherr in German-speaking countries]
-
A.
equivalentTo
chosen
Indicates that two entities represent the same concept, value, or state, and can be treated as interchangeable in the given context.
-
B.
materiallySimilarTo
Indicates that two entities share substantially the same physical or material characteristics, composition, or properties, though they may not be exactly identical.
-
C.
hasEquivalent
Indicates that two entities are considered equal in value, meaning, or function within a given context.
-
D.
isAlternativeTo
Indicates that one entity can serve as a substitute or different option in place of another.
-
E.
hasGrammaticalSimilarityTo
Indicates that two linguistic elements share similar grammatical structure, form, or function.
- 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_69bd463f156881908a99aca69c5721ac |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd589e35808190aa609bb04b128dbe |
completed | March 20, 2026, 2:24 p.m. |
| PD | Predicate disambiguation | batch_69bd5227063c8190973155a875b013a7 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:09 p.m.