Triple
T8937955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin-Lichtenberg |
E212823
|
entity |
| Predicate | containsLocality |
P45140
|
FINISHED |
| Object | Lichtenberg |
E38727
|
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: Lichtenberg | Statement: [Berlin-Lichtenberg, containsLocality, Lichtenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lichtenberg Context triple: [Berlin-Lichtenberg, containsLocality, Lichtenberg]
-
A.
Lichtenberg
chosen
Lichtenberg is a borough in eastern Berlin, Germany, known for its mix of residential areas, historical sites, and former Soviet administrative and military facilities.
-
B.
Reichenbach
Reichenbach is a German surname most notably associated with Hans Reichenbach, a prominent 20th-century philosopher of science and logical empiricist.
-
C.
Wurmberg
Wurmberg is a prominent mountain in the Harz range of central Germany, popular for skiing, hiking, and panoramic views.
-
D.
Kleeberg
Kleeberg is a Polish surname most notably associated with General Franciszek Kleeberg, a commander in the early stages of World War II.
-
E.
Lilienthal
Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
- 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_69ca839694c88190b324ffeb43d23b08 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66b57a348190979effe4f9998eb7 |
completed | April 1, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1e692548190b631c4926927d12f |
completed | April 3, 2026, 1:34 p.m. |
Created at: March 30, 2026, 6:58 p.m.