Triple
T15925000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hufeisenplan |
E386183
|
entity |
| Predicate | hasGermanTerm |
P6492
|
FINISHED |
| Object | Hufeisenplan |
—
|
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: Hufeisenplan | Statement: [Hufeisenplan, hasGermanTerm, Hufeisenplan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGermanTerm Context triple: [Hufeisenplan, hasGermanTerm, Hufeisenplan]
-
A.
meaningInGerman
Indicates that one entity expresses the meaning or translation of another entity in the German language.
-
B.
abbreviationGerman
Indicates that one term is the German-language abbreviation or shortened form of another term.
-
C.
hasTitleInGerman
chosen
Indicates that an entity has a specific title or name expressed in the German language.
-
D.
correspondsToAbbreviationInGerman
Indicates that one entity is the full form or concept for which the other entity serves as the corresponding abbreviation in the German language.
-
E.
hasEnglishGloss
Indicates that one entity serves as the English-language gloss or explanatory translation for the other entity.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:52 a.m.