Triple
T1594212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spree |
E34242
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Spandau |
E78225
|
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: Spandau | Statement: [Spree, flowsThrough, Spandau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spandau Context triple: [Spree, flowsThrough, Spandau]
-
A.
Spandau
chosen
Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
-
B.
Sachsenhausen
Sachsenhausen is a historic and culturally vibrant district of Frankfurt am Main, known for its traditional apple wine taverns, museums, and picturesque old town streets.
-
C.
Charlottenburg
Charlottenburg is a historic district in western Berlin, Germany, known for its baroque Charlottenburg Palace and role as a former independent city before incorporation into Berlin.
-
D.
Lichterfelde
Lichterfelde is a residential district in southwestern Berlin known for its historic villas, leafy streets, and affluent character.
-
E.
Kreuzberg
Kreuzberg is a vibrant, historically working-class district in central Berlin known for its multicultural community, alternative culture, and lively arts and nightlife scenes.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9092aed308190a0198a0fb977a9e5 |
completed | March 5, 2026, 4:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae0aaae76c81909707184b3a3d87d5 |
completed | March 8, 2026, 11:47 p.m. |
Created at: March 4, 2026, 7:27 p.m.