Triple
T9824848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilm Hosenfeld |
E238629
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hosenfeld |
E502087
|
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: Hosenfeld | Statement: [Wilm Hosenfeld, familyName, Hosenfeld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hosenfeld Context triple: [Wilm Hosenfeld, familyName, Hosenfeld]
-
A.
Hosenfeld
chosen
Hosenfeld is a German surname most notably associated with Wilm Hosenfeld, a Wehrmacht officer known for helping to save Jews during World War II.
-
B.
Heurich
Heurich is a German surname most notably associated with Christian Heurich, a prominent brewer and businessman in Washington, D.C.
-
C.
Schiffhauer
Schiffhauer is a surname, likely a variant of the German family name "Schiff."
-
D.
Zaslofsky
Zaslofsky is a surname most notably associated with Max Zaslofsky, an early star guard in the National Basketball Association.
-
E.
Braunshardt
Braunshardt is a district of the town of Weiterstadt in the state of Hesse, Germany.
- 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_69ca84e0dd1881909800765d1e21f735 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3181c688190afea3b27ee392a30 |
completed | April 2, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc810bac8190a5ff94c0717e7706 |
completed | April 5, 2026, 2:44 a.m. |
Created at: March 30, 2026, 8:31 p.m.