Triple
T1615043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leo Baeck |
E34697
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Baeck |
E172540
|
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: Baeck | Statement: [Leo Baeck, familyName, Baeck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baeck Context triple: [Leo Baeck, familyName, Baeck]
-
A.
Braunlage
Braunlage is a German town and ski resort in the Harz Mountains, known for its winter sports, hiking opportunities, and scenic natural surroundings.
-
B.
Hahn
chosen
Hahn is a surname of German origin borne by various notable individuals across fields such as science, sports, and the arts.
-
C.
Blaustein
Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
-
D.
Baeggu
Baeggu is an Oceanic language of the Meso-Melanesian group spoken by a small community in the Solomon Islands.
-
E.
Monowitz-Buna
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.
- 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_69a885ffc5ec819091afa325d5f9611c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9098f384c81909ef836ee779466e2 |
completed | March 5, 2026, 4:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad51ca2bc48190abb83f4d84782334 |
completed | March 8, 2026, 10:39 a.m. |
Created at: March 4, 2026, 7:28 p.m.