Triple
T13040058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Front Range, Colorado |
E327167
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Keenesburg
Keenesburg is a small town in northeastern Colorado known for its rural character and proximity to the Denver metropolitan area.
|
E1017219
|
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: Keenesburg | Statement: [Front Range, Colorado, containsCity, Keenesburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keenesburg Context triple: [Front Range, Colorado, containsCity, Keenesburg]
-
A.
Kanesville
Kanesville was the mid-19th-century Mormon settlement that later became the city of Council Bluffs, Iowa.
-
B.
Paynesville
Paynesville is a major city in Liberia, located near the capital Monrovia and known for its role as a key urban and sporting center in the country.
-
C.
Juneautown
Juneautown was one of the original 19th-century settlements that later became part of the city of Milwaukee, Wisconsin.
-
D.
Gardnersville
Gardnersville is a suburban township and residential area located near Monrovia in Liberia.
-
E.
Barberton
Barberton is a small industrial city in northeastern Ohio known historically for its manufacturing base and proximity to Akron.
- 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: Keenesburg Triple: [Front Range, Colorado, containsCity, Keenesburg]
Generated description
Keenesburg is a small town in northeastern Colorado known for its rural character and proximity to the Denver metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Keenesburg Target entity description: Keenesburg is a small town in northeastern Colorado known for its rural character and proximity to the Denver metropolitan area.
-
A.
Kanesville
Kanesville was the mid-19th-century Mormon settlement that later became the city of Council Bluffs, Iowa.
-
B.
Paynesville
Paynesville is a major city in Liberia, located near the capital Monrovia and known for its role as a key urban and sporting center in the country.
-
C.
Juneautown
Juneautown was one of the original 19th-century settlements that later became part of the city of Milwaukee, Wisconsin.
-
D.
Gardnersville
Gardnersville is a suburban township and residential area located near Monrovia in Liberia.
-
E.
Barberton
Barberton is a small industrial city in northeastern Ohio known historically for its manufacturing base and proximity to Akron.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d9804d8e3081909584c93df099859a |
completed | April 10, 2026, 10:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbd2f6a481909fdd418e7ad3cc22 |
completed | May 3, 2026, 4:15 a.m. |
| NEDg | Description generation | batch_69f6cd0d21e08190855dcbee000fc25d |
completed | May 3, 2026, 4:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6ce6b220c8190b1f49a9b2bfce692 |
completed | May 3, 2026, 4:26 a.m. |
Created at: April 9, 2026, 8:55 p.m.