Triple
T13036075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 42 (school) |
E326563
|
entity |
| Predicate | hasCampus |
P116
|
FINISHED |
| Object | 42 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: 42 Wolfsburg | Statement: [42 (school), hasCampus, 42 Wolfsburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 42 Wolfsburg Context triple: [42 (school), hasCampus, 42 Wolfsburg]
-
A.
VfL Wolfsburg
VfL Wolfsburg is a German professional football club based in Wolfsburg, best known for competing in the Bundesliga and winning the league title in 2009.
-
B.
Werder Bremen
Werder Bremen is a German professional football club based in Bremen, known for its long Bundesliga history and multiple national and international titles.
-
C.
Schalke 04
Schalke 04 is a professional German football club based in Gelsenkirchen, renowned for its passionate fan base and history in the Bundesliga and European competitions.
-
D.
Wolfsburg
chosen
Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
-
E.
Hannover 96
Hannover 96 is a professional German football club based in Hanover, best known for competing in the Bundesliga and having a long history dating back to the late 19th century.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbcf11f88190ab1746f973132af1 |
completed | May 3, 2026, 4:15 a.m. |
Created at: April 9, 2026, 8:55 p.m.