Triple
T1293191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klara Hitler |
E27592
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Klara |
E94446
|
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: [Klara Hitler, givenName, Klara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klara Context triple: [Klara Hitler, givenName, Klara]
-
A.
Clara
chosen
Clara is a feminine given name of Latin origin, derived from "clarus" meaning "bright" or "famous."
-
B.
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.
-
C.
Tanya
Tanya is the foundational Chabad-Lubavitch Hasidic work by Rabbi Shneur Zalman of Liadi, presenting a systematic approach to Jewish mysticism, psychology, and spiritual self-improvement.
-
D.
Milena
Milena is the birth name of actress Mila Kunis, a Ukrainian-born American performer known for roles in "That '70s Show" and "Black Swan."
-
E.
Sophie
Sophie is a feminine given name of Greek origin, commonly used in many countries and meaning "wisdom."
- 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_69a496d4ec448190ad653b2590c46711 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0f09d5c81909e6dc036fe9c5b4a |
completed | March 1, 2026, 10:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acacc1e7948190a1ecd240c751d258 |
completed | March 7, 2026, 10:54 p.m. |
Created at: March 1, 2026, 7:51 p.m.