Triple
T15473586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Böblingen district |
E376726
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Herrenberg |
E458163
|
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: Herrenberg | Statement: [Böblingen district, containsTown, Herrenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herrenberg Context triple: [Böblingen district, containsTown, Herrenberg]
-
A.
Herrenberg
chosen
Herrenberg is a historic town in the German state of Baden-Württemberg, known for its well-preserved medieval center and proximity to the Schönbuch Nature Park.
-
B.
Hammelburg
Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
-
C.
Günsberg
Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
-
D.
Höchheim
Höchheim is a small municipality in the Rhön-Grabfeld district of northern Bavaria, Germany.
-
E.
Grafenrheinfeld
Grafenrheinfeld is a small Bavarian town best known for hosting the former Grafenrheinfeld nuclear power plant on the Main River in northern Germany.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6e859481909c3d08343b7ad27c |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe6287f48190a54d0d029dc503f8 |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 3:34 a.m.