Triple
T1582152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westphalia |
E33788
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Minden |
E177242
|
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: Minden | Statement: [Westphalia, contains, Minden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Minden Context triple: [Westphalia, contains, Minden]
-
A.
Minden
chosen
Minden is a historic German city in North Rhine-Westphalia known for its strategic location on the Weser River and its well-preserved old town.
-
B.
Minden, Iowa
Minden, Iowa is a small rural city located in western Iowa within Pottawattamie County.
-
C.
Meridian
Meridian is a rapidly growing suburban city in southwestern Idaho, known for its family-friendly neighborhoods and proximity to Boise.
-
D.
Tubas
Tubas is a Palestinian city in the northeastern West Bank, serving as the administrative center of the Tubas Governorate and known for its agricultural surroundings in the Jordan Valley region.
-
E.
Eisenstadt
Eisenstadt is the small capital city of the Austrian state of Burgenland, known for its historic connection to composer Joseph Haydn and the Esterházy family.
- 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_69a885f27a4c8190a4622252cdf54c00 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a908ef80a48190bd5a8e51c65e5588 |
completed | March 5, 2026, 4:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad403355f4819084a560226a190b8a |
completed | March 8, 2026, 9:24 a.m. |
Created at: March 4, 2026, 7:27 p.m.