Triple
T19425654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Langensalza (1866) |
E485973
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Langensalza |
—
|
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: Langensalza | Statement: [Battle of Langensalza (1866), location, Langensalza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langensalza Context triple: [Battle of Langensalza (1866), location, Langensalza]
-
A.
Langensalza
Langensalza is a historic town in Thuringia, central Germany, known for its spa facilities and as the site of several notable battles.
-
B.
Langenbruck
Langenbruck is a small Swiss municipality in the canton of Basel-Landschaft, known for its scenic Jura landscape and rural character.
-
C.
Bad Langensalza
chosen
Bad Langensalza is a historic spa town in Thuringia, Germany, known for its thermal baths, well-preserved old town, and numerous themed gardens.
-
D.
Saalhof
Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
-
E.
Eilshausen
Eilshausen is a village and district within the municipality of Hiddenhausen in North Rhine-Westphalia, Germany.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63218772c8190a48b6cb01bd12b73 |
completed | April 20, 2026, 2:03 p.m. |
Created at: April 10, 2026, 1:37 p.m.