Triple
T15574995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onalaska |
E374344
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object |
Holmen
Holmen is a small village in La Crosse County, Wisconsin, that forms part of the La Crosse metropolitan area.
|
E1164576
|
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: Holmen | Statement: [Onalaska, hasNearbyCity, Holmen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Holmen Context triple: [Onalaska, hasNearbyCity, Holmen]
-
A.
Holmen
Holmen is a residential neighborhood in Oslo, Norway, known for its green surroundings and location within the borough of Vestre Aker.
-
B.
Holmen
Holmen is a historic waterfront district in Copenhagen, Denmark, known for its former naval base, repurposed industrial buildings, and vibrant cultural and residential developments.
-
C.
Hafslund
Hafslund is a major Norwegian energy and utility company known for its role in electricity production, distribution, and related services.
-
D.
Hestnes
Hestnes is a small settlement located within the municipality of Eigersund in Rogaland county, southwestern Norway.
-
E.
Haslum
Haslum is a suburban area in Bærum, Norway, known for its residential neighborhoods and proximity to Oslo.
- 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: Holmen Triple: [Onalaska, hasNearbyCity, Holmen]
Generated description
Holmen is a small village in La Crosse County, Wisconsin, that forms part of the La Crosse metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Holmen Target entity description: Holmen is a small village in La Crosse County, Wisconsin, that forms part of the La Crosse metropolitan area.
-
A.
Holmen
Holmen is a residential neighborhood in Oslo, Norway, known for its green surroundings and location within the borough of Vestre Aker.
-
B.
Holmen
Holmen is a historic waterfront district in Copenhagen, Denmark, known for its former naval base, repurposed industrial buildings, and vibrant cultural and residential developments.
-
C.
Hafslund
Hafslund is a major Norwegian energy and utility company known for its role in electricity production, distribution, and related services.
-
D.
Hestnes
Hestnes is a small settlement located within the municipality of Eigersund in Rogaland county, southwestern Norway.
-
E.
Haslum
Haslum is a suburban area in Bærum, Norway, known for its residential neighborhoods and proximity to Oslo.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e2140388190a8df7b835eaa72ce |
completed | April 16, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4978ec8190a57de5d9a2ec6653 |
completed | May 9, 2026, 3:01 p.m. |
| NEDg | Description generation | batch_69ff4d0678648190b61fbe79a60da8ec |
completed | May 9, 2026, 3:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff4d9258148190b21201bb09e16999 |
completed | May 9, 2026, 3:06 p.m. |
Created at: April 10, 2026, 4:10 a.m.