Triple
T17820745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rice County |
E444974
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Nerstrand
Nerstrand is a small city in southeastern Minnesota known for its proximity to Nerstrand-Big Woods State Park and its rural, community-oriented character.
|
E1289931
|
NE FINISHED |
How this triple was built (4 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: Nerstrand | Statement: [Rice County, contains, Nerstrand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nerstrand Context triple: [Rice County, contains, Nerstrand]
-
A.
Elsterberg
Elsterberg is a small town in the Vogtland region of Saxony, Germany, known for its historic castle ruins and scenic location along the White Elster River.
-
B.
Nylund
Nylund is a Scandinavian-origin surname most widely recognized through the fictional character Rose Nylund from the television series "The Golden Girls."
-
C.
Norén
Norén is a Swedish surname, notably borne by actress Noomi Rapace before she adopted her stage name.
-
D.
Nannfeldt
Nannfeldt was a mycologist and taxonomist known for his influential work on the classification and nomenclature of fungi, particularly within the Ascomycota.
-
E.
Iveland
Iveland is a small rural municipality in southern Norway known for its forests, agriculture, and mineral resources.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nerstrand Triple: [Rice County, contains, Nerstrand]
Generated description
Nerstrand is a small city in southeastern Minnesota known for its proximity to Nerstrand-Big Woods State Park and its rural, community-oriented character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nerstrand Target entity description: Nerstrand is a small city in southeastern Minnesota known for its proximity to Nerstrand-Big Woods State Park and its rural, community-oriented character.
-
A.
Elsterberg
Elsterberg is a small town in the Vogtland region of Saxony, Germany, known for its historic castle ruins and scenic location along the White Elster River.
-
B.
Nylund
Nylund is a Scandinavian-origin surname most widely recognized through the fictional character Rose Nylund from the television series "The Golden Girls."
-
C.
Norén
Norén is a Swedish surname, notably borne by actress Noomi Rapace before she adopted her stage name.
-
D.
Nannfeldt
Nannfeldt was a mycologist and taxonomist known for his influential work on the classification and nomenclature of fungi, particularly within the Ascomycota.
-
E.
Iveland
Iveland is a small rural municipality in southern Norway known for its forests, agriculture, and mineral resources.
- F. None of above. chosen
Provenance (5 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48910eb8881908db8ec08e2752d7d |
completed | April 19, 2026, 7:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02ff680b548190b6f1fbc09335ff31 |
completed | May 12, 2026, 10:22 a.m. |
| NEDg | Description generation | batch_6a0300ca1ac88190b474ac8276d55445 |
completed | May 12, 2026, 10:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03017f7ea08190b3a98db1c429d04f |
completed | May 12, 2026, 10:31 a.m. |
Created at: April 10, 2026, 10:15 a.m.