Triple
T9223352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schelklingen |
E221616
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | district Ingstetten |
E783729
|
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: district Ingstetten | Statement: [Schelklingen, hasSubdivision, district Ingstetten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: district Ingstetten Context triple: [Schelklingen, hasSubdivision, district Ingstetten]
-
A.
Gerstetten
Gerstetten is a municipality in the Heidenheim district of the state of Baden-Württemberg in southern Germany.
-
B.
Engstingen
chosen
Engstingen is a municipality in the state of Baden-Württemberg in southwestern Germany, situated on the Swabian Alb plateau.
-
C.
Söhnstetten
Söhnstetten is a village in the Heidenheim district of Baden-Württemberg, Germany, known as a part of the municipality of Steinheim am Albuch on the Swabian Jura.
-
D.
Impflingen
Impflingen is a small municipality in the state of Rhineland-Palatinate in southwestern Germany.
-
E.
Stetten
Stetten is a locality within the town of Lichtenfels in the German state of Bavaria.
- 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_69ca83ec8db08190a9110df8232885d2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda7903208190b4e29a1591aab78a |
completed | April 1, 2026, 8:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0664565748190a45cf382fca72cbe |
completed | April 4, 2026, 1:15 a.m. |
Created at: March 30, 2026, 7:28 p.m.