Triple
T23475011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nannestad |
E570237
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Stein |
—
|
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: Stein | Statement: [Nannestad, hasSettlement, Stein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stein Context triple: [Nannestad, hasSettlement, Stein]
-
A.
Stein
Stein is a German-origin surname borne by numerous notable individuals across fields such as exploration, philosophy, literature, and politics.
-
B.
Stein
Stein is a municipality in the Dutch province of Limburg, known for its industrial heritage and location along the Meuse River near the Belgian border.
-
C.
Stein
chosen
Stein is a nearby settlement to Halistra, likely a small village or town in the same region.
-
D.
Stein
Stein is a locality that forms one of the subdivisions of the town of Hechingen in the German state of Baden-Württemberg.
-
E.
Stein and Day
Stein and Day was an American publishing company known for releasing popular fiction and nonfiction titles during the mid-20th century.
- 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_69e245af8a88819084f2704f6d265a92 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a704e2a48190acb55f77a2124412 |
completed | April 29, 2026, 6:36 a.m. |
Created at: April 17, 2026, 6 p.m.