Triple
T4820587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedenau |
E107699
|
entity |
| Predicate | officialName |
P66
|
FINISHED |
| Object | Friedenau |
E107699
|
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: Friedenau | Statement: [Friedenau, officialName, Friedenau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Friedenau Context triple: [Friedenau, officialName, Friedenau]
-
A.
Friedenau
chosen
Friedenau is a residential district in southwestern Berlin known for its historic architecture, leafy streets, and literary heritage.
-
B.
Schaafheim
Schaafheim is a municipality in the state of Hesse in central Germany.
-
C.
Ziegenhain
Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
-
D.
Entzheim
Entzheim is a commune in northeastern France, near Strasbourg, best known for hosting Strasbourg Airport.
-
E.
Marlenheim
Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
- 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_69bd43f9efa081908314cb3e94fa1695 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6c98358081908ed43425af667a98 |
completed | March 20, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beb0cd44088190ba26171758898497 |
completed | March 21, 2026, 2:53 p.m. |
Created at: March 20, 2026, 1:24 p.m.