Triple
T22435556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schlossplatz Höchst |
E554612
|
entity |
| Predicate | nearby |
P350
|
FINISHED |
| Object | Altstadt Höchst |
—
|
NE NERFINISHED |
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 Höchst | Statement: [Schlossplatz Höchst, nearby, Altstadt Höchst]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Altstadt Höchst Context triple: [Schlossplatz Höchst, nearby, Altstadt Höchst]
-
A.
Bockenheim
Bockenheim is a lively urban district of Frankfurt am Main known for its mix of residential areas, shops, and university facilities.
-
B.
Höchst
Höchst is a municipality in the Austrian state of Vorarlberg, located near the Swiss border along the Rhine River.
-
C.
Höchst
chosen
Höchst is a historic district in western Frankfurt am Main, Germany, known for its well-preserved old town and former industrial and chemical industry sites.
-
D.
Schlossplatz Höchst
Schlossplatz Höchst is the central historic square in the Höchst district of Frankfurt am Main, known for its traditional architecture and proximity to the local castle and old town.
-
E.
Altstadt (Frankfurt am Main)
Altstadt (Frankfurt am Main) is the historic old town district of Frankfurt, Germany, known for its reconstructed medieval streets, traditional architecture, and major cultural landmarks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15adda0e48190825a5b705ae52d5b |
completed | April 29, 2026, 1:11 a.m. |
Created at: April 16, 2026, 8:47 p.m.