Triple
T12994717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annelies Marie Frank |
E322005
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Annelies |
E322005
|
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: Annelies | Statement: [Annelies Marie Frank, givenName, Annelies]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annelies Context triple: [Annelies Marie Frank, givenName, Annelies]
-
A.
Annelies
chosen
Annelies is the given first name of Anne Frank, the Jewish diarist whose writings from hiding during the Holocaust became world-famous.
-
B.
Hanna Hilsdorf
Hanna Hilsdorf is a German actress known for her role in the crime drama film "In the Fade" and for her work in contemporary German cinema and television.
-
C.
Annemarie Schön
Annemarie Schön was the wife of renowned German football coach Helmut Schön.
-
D.
Christa
Christa was the first name of Christa McAuliffe, the American teacher and astronaut selected as the first private citizen to fly in space.
-
E.
Arlette
Arlette is the given first name of renowned Brazilian actress Fernanda Montenegro, a leading figure in Brazilian theater, film, and television.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7877f481908a03f1077600e58a |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c0fca5e4819086b010fdd1813419 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:44 p.m.