Triple
T1863164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tempelhof-Schöneberg |
E34860
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mariendorf |
E79213
|
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: Mariendorf | Statement: [Tempelhof-Schöneberg, contains, Mariendorf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mariendorf Context triple: [Tempelhof-Schöneberg, contains, Mariendorf]
-
A.
Langendorf
Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
-
B.
Fürstenwalde
Fürstenwalde is a town in eastern Germany’s Brandenburg region, known for its location on the River Spree and its historic churches and medieval architecture.
-
C.
Alt-Mariendorf
chosen
Alt-Mariendorf is a Berlin U-Bahn station in the Mariendorf district that serves as the southern terminus of line U6.
-
D.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
E.
Schkopau
Schkopau is a municipality in the Saalekreis district of Saxony-Anhalt, Germany, known for its large chemical industry complex.
- 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_69a88600b2f88190bc09303e68ab517e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb09f856c8190807a7cf2a5f49fcb |
completed | March 7, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71a242f081908179251c120dd229 |
completed | March 9, 2026, 7:07 a.m. |
Created at: March 4, 2026, 7:34 p.m.