Triple
T15586810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CF |
E374642
|
entity |
| Predicate | equivalentTermInGerman |
P56110
|
FINISHED |
| Object | BR |
—
|
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: BR | Statement: [CF, equivalentTermInGerman, BR]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentTermInGerman Context triple: [CF, equivalentTermInGerman, BR]
-
A.
equivalentSurnameInGerman
Indicates that two surnames are equivalent to each other when translated into or represented in the German language.
-
B.
equivalentIn
Indicates that two entities are considered logically or functionally the same in meaning, status, or effect within a given context.
-
C.
meaningInGerman
chosen
Indicates that one entity expresses the meaning or translation of another entity in the German language.
-
D.
languageEquivalent
Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
-
E.
nameInGerman
Indicates that an entity is known or referred to by a specific name in the German language.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e4900408190aadb48b001db4169 |
completed | April 16, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69deda817e9881909b0c66fc9056f7d5 |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:11 a.m.