Triple
T11856927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gahanna, Ohio |
E282062
|
entity |
| Predicate | hasPark |
P105
|
FINISHED |
| Object |
Hannah Park
Hannah Park is a public recreational park located in Gahanna, Ohio, offering outdoor spaces and amenities for community activities and leisure.
|
E949125
|
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: Hannah Park | Statement: [Gahanna, Ohio, hasPark, Hannah Park]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hannah Park Context triple: [Gahanna, Ohio, hasPark, Hannah Park]
-
A.
Hyein Park
Hyein Park is a Korean-Canadian voice actress best known for voicing the character Abby in Pixar’s animated film "Turning Red."
-
B.
Da-yeon Jung
Da-yeon Jung is a Korean individual notable enough to be recognized as a prominent bearer of the surname Jung.
-
C.
Saemi Kim
Saemi Kim is a film and television producer known for her work behind the scenes bringing scripted projects to fruition.
-
D.
Jane Kim
Jane Kim is an American politician and attorney who served on the San Francisco Board of Supervisors and is known for her progressive advocacy on housing, education, and workers’ rights.
-
E.
Willa Kim
Willa Kim was an acclaimed American costume designer known for her vibrant, innovative work on Broadway, ballet, and opera, earning multiple Tony Awards over her career.
- 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: Hannah Park Triple: [Gahanna, Ohio, hasPark, Hannah Park]
Generated description
Hannah Park is a public recreational park located in Gahanna, Ohio, offering outdoor spaces and amenities for community activities and leisure.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hannah Park Target entity description: Hannah Park is a public recreational park located in Gahanna, Ohio, offering outdoor spaces and amenities for community activities and leisure.
-
A.
Hyein Park
Hyein Park is a Korean-Canadian voice actress best known for voicing the character Abby in Pixar’s animated film "Turning Red."
-
B.
Da-yeon Jung
Da-yeon Jung is a Korean individual notable enough to be recognized as a prominent bearer of the surname Jung.
-
C.
Saemi Kim
Saemi Kim is a film and television producer known for her work behind the scenes bringing scripted projects to fruition.
-
D.
Jane Kim
Jane Kim is an American politician and attorney who served on the San Francisco Board of Supervisors and is known for her progressive advocacy on housing, education, and workers’ rights.
-
E.
Willa Kim
Willa Kim was an acclaimed American costume designer known for her vibrant, innovative work on Broadway, ballet, and opera, earning multiple Tony Awards over her career.
- 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_69d6ab287ba48190a5178779fd19b9b7 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a699089c8190b7a298baf13dcded |
completed | April 10, 2026, 7:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f167d9b9e8819093582637941fc5ca |
completed | April 29, 2026, 2:07 a.m. |
| NEDg | Description generation | batch_69f17006e6108190b51b20ddf6d2368c |
completed | April 29, 2026, 2:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f17819af5c8190a98db3cd8eff8da2 |
completed | April 29, 2026, 3:16 a.m. |
Created at: April 8, 2026, 9:43 p.m.