Triple
T18281038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monowitz labor camp |
E437863
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Monowitz-Buna |
—
|
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: Monowitz-Buna | Statement: [Monowitz labor camp, alsoKnownAs, Monowitz-Buna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monowitz-Buna Context triple: [Monowitz labor camp, alsoKnownAs, Monowitz-Buna]
-
A.
Monowitz-Buna
chosen
Monowitz-Buna was a Nazi German concentration and forced-labor camp near Auschwitz, primarily used to supply slave labor for the IG Farben synthetic rubber and fuel plant during World War II.
-
B.
Basedow
Basedow is a small village in northern Germany’s Mecklenburg region, known for its historic manor house and picturesque rural landscape.
-
C.
Matorf
Matorf is a district or locality within the town of Lemgo in the German state of North Rhine-Westphalia.
-
D.
Biesenthal
Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
-
E.
Lautenthal
Lautenthal is a small historic mining town in Germany’s Harz Mountains, known for its picturesque valley setting and former silver mining industry.
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50056ea0481908d66bf263ac80c75 |
completed | April 19, 2026, 4:18 p.m. |
Created at: April 10, 2026, 10:35 a.m.