Triple
T1489469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aller |
E29543
|
entity |
| Predicate | flowsNear |
P350
|
FINISHED |
| Object | Wolfsburg |
E74139
|
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: Wolfsburg | Statement: [Aller, flowsNear, Wolfsburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wolfsburg Context triple: [Aller, flowsNear, Wolfsburg]
-
A.
Wolfsburg
chosen
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
B.
Dortmund
Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
-
C.
Ingolstadt
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.
-
D.
Delmenhorst
Delmenhorst is a mid-sized industrial and commuter city in northwestern Germany, located near Bremen in the federal state of Lower Saxony.
-
E.
Braunschweig
Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6a6095481909e9d406ac9a41828 |
completed | March 1, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8aad1a30819086df8e4ef3752263 |
completed | March 8, 2026, 2:41 p.m. |
Created at: March 1, 2026, 8:12 p.m.