Triple
T7104828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verena |
E165554
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Vreni |
E165554
|
NE 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: Vreni | Statement: [Verena, hasDiminutive, Vreni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vreni Context triple: [Verena, hasDiminutive, Vreni]
-
A.
Verena
chosen
Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
-
B.
Christa
Christa was the first name of Christa McAuliffe, the American teacher and astronaut selected as the first private citizen to fly in space.
-
C.
Odile
Odile is the seductive and deceptive Black Swan character in the ballet "Swan Lake," often portrayed as the antagonist and foil to the virtuous Odette.
-
D.
Franziska
Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
-
E.
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6887fcddc8190a5d58908f6dee590 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e58b3f708190bebca7d4c4db40f2 |
completed | March 27, 2026, 8:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7a3231f9c8190a19ddff3f5bf7cac |
completed | March 28, 2026, 9:45 a.m. |
Created at: March 27, 2026, 2:42 p.m.