Triple
T22591334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margarete Steiff |
E564956
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Margarete |
—
|
NE NERFINISHED |
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: Margarete | Statement: [Margarete Steiff, givenName, Margarete]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margarete Context triple: [Margarete Steiff, givenName, Margarete]
-
A.
Margarete
chosen
Margarete is a female given name of Greek origin, commonly associated with the meaning "pearl" and used in various European languages.
-
B.
Margarethe
"Margarethe" is a major painting by German artist Anselm Kiefer that reflects on German history and memory, often interpreted in relation to the Holocaust and cultural guilt.
-
C.
Margarethe Cammermeyer
Margarethe Cammermeyer is a retired U.S. Army colonel and nurse who became a prominent LGBTQ+ rights figure after being discharged for being a lesbian and successfully challenging the military’s ban on gay service members.
-
D.
Elfriede
Elfriede is a feminine given name of German origin, notably borne by Austrian Nobel Prize–winning writer Elfriede Jelinek.
-
E.
Martha Hagen
Martha Hagen is known as the wife of American comedian, actor, and writer Michael Ian Black.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245836014819091b91ed3074742a3 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f16160651081909d23735336fd7a16 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 2:48 p.m.