Triple
T3901343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ERC Ingolstadt |
E90495
|
entity |
| Predicate | basedIn |
P40
|
FINISHED |
| Object | Ingolstadt, Germany |
E130045
|
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: Ingolstadt, Germany | Statement: [ERC Ingolstadt, basedIn, Ingolstadt, Germany]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ingolstadt, Germany Context triple: [ERC Ingolstadt, basedIn, Ingolstadt, Germany]
-
A.
Ingolstadt
chosen
Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
-
B.
Weinheim, Germany
Weinheim, Germany is a town in the state of Baden-Württemberg known for its historic old town, twin castles, and role as a regional economic and publishing center.
-
C.
Deggendorf, Germany
Deggendorf, Germany is a Bavarian town on the Danube River known as a regional commercial and industrial center with strong ties to manufacturing and technology companies.
-
D.
Dingolfing, Germany
Dingolfing, Germany is a Bavarian town known as one of BMW’s largest and most important automobile production sites.
-
E.
Donauwörth, Germany
Donauwörth, Germany is a Bavarian town on the Danube River known as a regional industrial hub and major site of helicopter production.
- 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecf2f230819099abc109a0b7d916 |
completed | March 9, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51ca7636081908f98c4e22617f808 |
completed | March 14, 2026, 8:30 a.m. |
Created at: March 9, 2026, 3:21 p.m.