Triple
T30050943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Lawn Cemetery, Columbus, Ohio |
E763600
|
entity |
| Predicate | hasNotableBurial |
P196
|
FINISHED |
| Object |
James A. Rhodes
James A. Rhodes was a long-serving Republican governor of Ohio known for his pro-business policies and controversial handling of the 1970 Kent State University protests.
|
E1897422
|
NE FINISHED |
How this triple was built (2 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: James A. Rhodes | Statement: [Green Lawn Cemetery, Columbus, Ohio, hasNotableBurial, James A. Rhodes]
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: James A. Rhodes Triple: [Green Lawn Cemetery, Columbus, Ohio, hasNotableBurial, James A. Rhodes]
Generated description
James A. Rhodes was a long-serving Republican governor of Ohio known for his pro-business policies and controversial handling of the 1970 Kent State University protests.
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_69f22470a89c8190be7273297c0e0d19 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67a1567c0819095851e4b9a9c322b |
completed | May 2, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a273243ba608190ae65fa6969af4dbc |
completed | June 8, 2026, 9:21 p.m. |
| NEDg | Description generation | batch_6a2738118ef881908a90a7a5d0879325 |
completed | June 8, 2026, 9:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a273897eefc81908a57b53cb71a83a2 |
completed | June 8, 2026, 9:48 p.m. |
Created at: April 29, 2026, 6:55 p.m.