Triple
T27062955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O. Max Gardner |
E685091
|
entity |
| Predicate | hasHonor |
P11
|
FINISHED |
| Object |
O. Max Gardner Award
The O. Max Gardner Award is a prestigious honor presented by the University of North Carolina system to recognize faculty who have made the greatest contributions to the welfare of the human race.
|
E1754115
|
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: O. Max Gardner Award | Statement: [O. Max Gardner, hasHonor, O. Max Gardner Award]
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: O. Max Gardner Award Triple: [O. Max Gardner, hasHonor, O. Max Gardner Award]
Generated description
The O. Max Gardner Award is a prestigious honor presented by the University of North Carolina system to recognize faculty who have made the greatest contributions to the welfare of the human race.
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_69ef14835fcc81908bd737b4267ae528 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f622e5c8e4819090baecc212ac8a21 |
completed | May 2, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a123ad3e1508190804d758f3b043492 |
completed | May 23, 2026, 11:40 p.m. |
| NEDg | Description generation | batch_6a123b542138819086f001a5c2dcd76b |
completed | May 23, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a123bf84c28819096727646233344f5 |
completed | May 23, 2026, 11:44 p.m. |
Created at: April 27, 2026, 8:23 a.m.