Triple
T22309499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stark County |
E551475
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Massillon
Massillon is a mid-sized industrial city in northeastern Ohio known for its steel industry heritage and storied high school football tradition.
|
E1529817
|
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: Massillon | Statement: [Stark County, hasCity, Massillon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Massillon Context triple: [Stark County, hasCity, Massillon]
-
A.
Monessen
Monessen is a small industrial city in western Pennsylvania historically known for its steel production along the Monongahela River.
-
B.
Middlestown
Middlestown is a village in West Yorkshire, England, situated within the metropolitan borough of the City of Wakefield.
-
C.
Diggstown
Diggstown is a 1992 sports comedy-drama film centered on a high-stakes boxing con set in a small Southern town.
-
D.
Carlisle, Ohio
Carlisle, Ohio is a small village in Warren and Montgomery counties known as a suburban community within the Dayton metropolitan area in southwestern Ohio.
-
E.
Ohioville, Pennsylvania
Ohioville, Pennsylvania is a small rural borough in Beaver County known for its residential communities and proximity to the Ohio River and the greater Pittsburgh metropolitan area.
- 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: Massillon Triple: [Stark County, hasCity, Massillon]
Generated description
Massillon is a mid-sized industrial city in northeastern Ohio known for its steel industry heritage and storied high school football tradition.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Massillon Target entity description: Massillon is a mid-sized industrial city in northeastern Ohio known for its steel industry heritage and storied high school football tradition.
-
A.
Monessen
Monessen is a small industrial city in western Pennsylvania historically known for its steel production along the Monongahela River.
-
B.
Middlestown
Middlestown is a village in West Yorkshire, England, situated within the metropolitan borough of the City of Wakefield.
-
C.
Diggstown
Diggstown is a 1992 sports comedy-drama film centered on a high-stakes boxing con set in a small Southern town.
-
D.
Carlisle, Ohio
Carlisle, Ohio is a small village in Warren and Montgomery counties known as a suburban community within the Dayton metropolitan area in southwestern Ohio.
-
E.
Ohioville, Pennsylvania
Ohioville, Pennsylvania is a small rural borough in Beaver County known for its residential communities and proximity to the Ohio River and the greater Pittsburgh metropolitan area.
- 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1574d53148190a1ec07f849e1ae9d |
completed | April 29, 2026, 12:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0acca39c4c8190a418ec2ff1434122 |
completed | May 18, 2026, 8:24 a.m. |
| NEDg | Description generation | batch_6a0acf8c37d48190990bc7e1152f98b3 |
completed | May 18, 2026, 8:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0acff349248190a13968f340cdebcf |
completed | May 18, 2026, 8:38 a.m. |
Created at: April 16, 2026, 8:42 p.m.