Triple
T28002122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fräulein Katharina |
E707174
|
entity |
| Predicate | languageOfOriginalReport |
P194740
|
FINISHED |
| Object | German |
—
|
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: German | Statement: [Fräulein Katharina, languageOfOriginalReport, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfOriginalReport Context triple: [Fräulein Katharina, languageOfOriginalReport, German]
-
A.
languageOfOriginalDescription
Indicates that something is expressed or documented in its initial or source language version.
-
B.
languageOfOriginalGrant
Indicates the language in which the original grant or granting document was written or issued.
-
C.
languageOfOfficialReports
Indicates the language in which an entity’s official reports are written or issued.
-
D.
originalLanguageText
Indicates that a text is expressed in its original, untranslated language.
-
E.
originalTextLanguage
Indicates the language in which a text was originally written or created before any translation or adaptation.
- F. None of above. chosen
Provenance (4 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_69ef96b980d88190a753b2f9a978595a |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69fd864235b481908738dbb69556bc62 |
completed | May 8, 2026, 6:44 a.m. |
| PD | Predicate disambiguation | batch_69fd8373b6bc819091c554f29ee17fec |
completed | May 8, 2026, 6:32 a.m. |
| PDg | Predicate description generation | batch_69fd8640e1d4819081c98f15eeb221ab |
completed | May 8, 2026, 6:44 a.m. |
Created at: April 27, 2026, 7:57 p.m.