Triple
T4225485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clara |
E94446
|
entity |
| Predicate | hasVariantSpelling |
P457
|
FINISHED |
| Object | Klara |
E339402
|
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: Klara | Statement: [Clara, hasVariantSpelling, Klara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klara Context triple: [Clara, hasVariantSpelling, Klara]
-
A.
Klara
chosen
Klara is the given first name of Hannelore Kohl, the late wife of former German Chancellor Helmut Kohl.
-
B.
Clara
Clara is a character in the American folk opera "Porgy and Bess," known as a young mother whose lullaby "Summertime" is one of the work’s most famous songs.
-
C.
Clara
Clara is a feminine given name of Latin origin, derived from "clarus" meaning "bright" or "famous."
-
D.
Lila
Lila is a central female character in Max Frisch’s novel "Mein Name sei Gantenbein," around whom the narrator constructs one of his imagined lives and relationships.
-
E.
Klara Dan
Klara Dan was a Hungarian-American mathematician and computer programmer known for her pioneering work on early digital computers alongside her husband, John von Neumann.
- 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_69b3453700a08190ae88792e3dc63207 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e4d32d481909df7b18f502945b8 |
completed | March 12, 2026, 11:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b59647bd948190afc168ba1057a52f |
completed | March 14, 2026, 5:09 p.m. |
Created at: March 12, 2026, 11:04 p.m.