Triple
T23069492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomery County, Kansas |
E575151
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Havana, Kansas
Havana, Kansas is a small rural city located in southeastern Kansas in the United States.
|
E1587829
|
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: Havana, Kansas | Statement: [Montgomery County, Kansas, contains, Havana, Kansas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Havana, Kansas Context triple: [Montgomery County, Kansas, contains, Havana, Kansas]
-
A.
Wellington, Kansas
Wellington, Kansas is a small city in south-central Kansas known historically as a regional agricultural and railroad hub.
-
B.
Hartford, Kansas
Hartford, Kansas is a small rural city in east-central Kansas known for its close-knit community and agricultural surroundings.
-
C.
Newton, Kansas
Newton, Kansas is a small city in south-central Kansas known historically as a railroad hub and gateway to the American West.
-
D.
Frankfort, Kansas
Frankfort, Kansas is a small rural city in Marshall County known for its agricultural community and location in northeastern Kansas.
-
E.
Toronto, Kansas
Toronto, Kansas is a small rural city in southeastern Kansas known for its proximity to Toronto Lake and Cross Timbers State Park.
- 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: Havana, Kansas Triple: [Montgomery County, Kansas, contains, Havana, Kansas]
Generated description
Havana, Kansas is a small rural city located in southeastern Kansas in the United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Havana, Kansas Target entity description: Havana, Kansas is a small rural city located in southeastern Kansas in the United States.
-
A.
Wellington, Kansas
Wellington, Kansas is a small city in south-central Kansas known historically as a regional agricultural and railroad hub.
-
B.
Hartford, Kansas
Hartford, Kansas is a small rural city in east-central Kansas known for its close-knit community and agricultural surroundings.
-
C.
Newton, Kansas
Newton, Kansas is a small city in south-central Kansas known historically as a railroad hub and gateway to the American West.
-
D.
Frankfort, Kansas
Frankfort, Kansas is a small rural city in Marshall County known for its agricultural community and location in northeastern Kansas.
-
E.
Toronto, Kansas
Toronto, Kansas is a small rural city in southeastern Kansas known for its proximity to Toronto Lake and Cross Timbers State Park.
- 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a676c08190863c034663406018 |
completed | April 29, 2026, 4:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c822d73608190bce952f3768f8cb1 |
completed | May 19, 2026, 3:30 p.m. |
| NEDg | Description generation | batch_6a0ca6eda1f481908482f3a66d576c3c |
completed | May 19, 2026, 6:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ca7bdf7f08190b50191b029480fc5 |
completed | May 19, 2026, 6:11 p.m. |
Created at: April 17, 2026, 3:55 p.m.