Triple
T6965616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AA battery |
E161479
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Mignon |
E627865
|
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: Mignon | Statement: [AA battery, alsoKnownAs, Mignon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mignon Context triple: [AA battery, alsoKnownAs, Mignon]
-
A.
Mignon
chosen
Mignon is a mysterious, ethereal child of Italian origin who becomes one of the most poignant and symbolically rich figures in Goethe’s novel "Wilhelm Meister’s Apprenticeship."
-
B.
Zibelle
Zibelle is a village in eastern Germany, historically part of Lusatia, known in this context as the place where physicist Walther Nernst died.
-
C.
Marzelline
Marzelline is a character in Beethoven's opera "Fidelio," portrayed as the jailer Rocco’s daughter who becomes romantically entangled with the disguised heroine.
-
D.
Capucine
Capucine was a French fashion model and film actress best known for her elegant screen presence in 1960s comedies and dramas, including roles in films like The Pink Panther.
-
E.
Margeride
Margeride is a mountainous and sparsely populated region in south-central France known for its granite plateaus, forests, and traditional rural landscapes.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db1049e0819097099a0e9d15f787 |
completed | March 27, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c758a57b8481908cef7de9b3abf7a3 |
completed | March 28, 2026, 4:27 a.m. |
Created at: March 27, 2026, 2:30 p.m.