Triple
T24539708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erzherzogin von Österreich |
E607055
|
entity |
| Predicate | wordByWordMeaning |
P156654
|
FINISHED |
| Object | Archduchess of Austria |
—
|
NE NERFINISHED |
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: Archduchess of Austria | Statement: [Erzherzogin von Österreich, wordByWordMeaning, Archduchess of Austria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wordByWordMeaning Context triple: [Erzherzogin von Österreich, wordByWordMeaning, Archduchess of Austria]
-
A.
textMeaning
Indicates that one text expresses, conveys, or corresponds to a particular meaning or semantic content.
-
B.
duMeaning
Indicates that one entity expresses, conveys, or signifies a particular meaning or sense in relation to another.
-
C.
ermenMeaning
Indicates that one entity represents or conveys the meaning or semantic interpretation of another entity.
-
D.
openingWordsMeaning
Indicates that the meaning or significance of a text is conveyed or characterized by its opening words.
-
E.
tegMeaning
Indicates that one entity expresses, conveys, or stands for the meaning or semantic content of another entity.
- 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_69e2c4c9bf94819082d05da6f5c29907 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2be044d4c819094e14eda28d371a7 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6b0ca8081908d931aec560eae56 |
completed | April 30, 2026, 12:47 a.m. |
| PDg | Predicate description generation | batch_69f2b8b8bc5881908df49c0b07110246 |
completed | April 30, 2026, 2:04 a.m. |
Created at: April 18, 2026, 2:26 a.m.