Triple
T8715102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viktualienmarkt |
E206873
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Altstadt-Lehel |
E206869
|
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: Altstadt-Lehel | Statement: [Viktualienmarkt, locatedIn, Altstadt-Lehel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Altstadt-Lehel Context triple: [Viktualienmarkt, locatedIn, Altstadt-Lehel]
-
A.
Altstadt-Lehel borough
chosen
Altstadt-Lehel is a central Munich borough that encompasses the historic Old Town and some of the city’s most prominent cultural and architectural landmarks.
-
B.
Bockenheim
Bockenheim is a lively urban district of Frankfurt am Main known for its mix of residential areas, shops, and university facilities.
-
C.
Altstadt
Altstadt is the historic old town of Zürich, Switzerland, known for its medieval streets, preserved architecture, and cultural landmarks along the Limmat River.
-
D.
Altstadt
Altstadt is the historic old town district of Dresden, Germany, known for its baroque architecture and major cultural landmarks.
-
E.
Altstadt
Altstadt is the historic old town district of Höchst, characterized by its preserved medieval architecture and traditional charm.
- 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_69ca83572d4881909bef3be2b578d539 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5cd6707c819092c9fca34f273d5e |
completed | March 31, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf516d9d9081909230441dd349dc9e |
completed | April 3, 2026, 5:34 a.m. |
Created at: March 30, 2026, 6:35 p.m.