Triple
T8886583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herta Oberheuser |
E211546
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Herta |
E574940
|
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: Herta | Statement: [Herta Oberheuser, givenName, Herta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herta Context triple: [Herta Oberheuser, givenName, Herta]
-
A.
Herta
chosen
Herta is a feminine given name of Germanic origin, commonly used in Central and Eastern Europe.
-
B.
Stella Carlin
Stella Carlin is a rebellious and charismatic inmate character from the television series "Orange Is the New Black."
-
C.
Mercedes Barcha
Mercedes Barcha was the longtime wife and muse of Nobel Prize–winning author Gabriel García Márquez, known for her steadfast support throughout his literary career.
-
D.
Anna Herdegen
Anna Herdegen was the mother of German organic chemist and Nobel laureate Hans Fischer.
-
E.
Toni Krinner
Toni Krinner was a German ice hockey coach and former player known for his coaching roles in the Deutsche Eishockey Liga.
- 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc618d4c188190810d2e38591f515a |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfabd9971c81909d1437a52e906813 |
completed | April 3, 2026, noon |
Created at: March 30, 2026, 6:53 p.m.