Triple
T3021971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A73 |
E82480
|
entity |
| Predicate | links |
P2535
|
FINISHED |
| Object | Lichtenfels |
E11679
|
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: Lichtenfels | Statement: [A73, links, Lichtenfels]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lichtenfels Context triple: [A73, links, Lichtenfels]
-
A.
Lichtenfels
chosen
Lichtenfels is a town in the Upper Franconia region of Bavaria, Germany, known for its basket-making tradition and historic architecture.
-
B.
Markranstädt
Markranstädt is a small town in the German state of Saxony, located near Leipzig and known for its local industry and proximity to the Kulkwitzer See recreation area.
-
C.
Lampoldshausen
Lampoldshausen is a German village best known as a major site for rocket propulsion research and testing facilities of the German Aerospace Center.
-
D.
Weiterstadt
Weiterstadt is a town in the German state of Hesse, located near Darmstadt and known for its residential areas and commercial centers.
-
E.
Mühlhausen
Mühlhausen is a historic town in central Germany, known for its well-preserved medieval architecture and cultural heritage.
- 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_69ad8b1fb34081908c1b873e2b7273e1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a963034819093d96566e9b0cea9 |
completed | March 8, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c3601894819082568a7ee8d6aabc |
completed | March 14, 2026, 2:09 a.m. |
Created at: March 8, 2026, 3 p.m.