Triple
T18244705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grüneberg |
E436919
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Grüneberg@en |
—
|
NE NERFINISHED |
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: Grüneberg@en | Statement: [Grüneberg, hasNameInLanguage, Grüneberg@en]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grüneberg@en Context triple: [Grüneberg, hasNameInLanguage, Grüneberg@en]
-
A.
Grüneberg
chosen
Grüneberg is a locality in Germany historically known as the site of the Battle of Grüneberg.
-
B.
Grünberg
Grünberg is a small German town in the state of Hesse, known for its historic half-timbered old town and traditional regional festivals.
-
C.
Grünfier
Grünfier is a small locality in the historical region of Pomerania, formerly part of Germany and now within modern-day Poland.
-
D.
Berggruen
Berggruen is a surname most prominently associated with the billionaire investor and philanthropist Nicholas Berggruen and his family.
-
E.
Grüsch
Grüsch is a Swiss municipality in the canton of Graubünden, situated in the alpine Prättigau valley and known as a gateway to nearby mountain and ski areas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b91104e08190a8241f7d260a5162 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f7e5d63081908d0e6249578867a1 |
completed | April 19, 2026, 3:42 p.m. |
Created at: April 10, 2026, 10:33 a.m.