Triple
T19554137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Publix |
E489262
|
entity |
| Predicate | hasKeyPerson |
P256
|
FINISHED |
| Object |
Ed Crenshaw
Ed Crenshaw is an American business executive best known for serving as the CEO of Publix Super Markets.
|
E1404355
|
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: Ed Crenshaw | Statement: [Publix, hasKeyPerson, Ed Crenshaw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ed Crenshaw Context triple: [Publix, hasKeyPerson, Ed Crenshaw]
-
A.
Dan Crawford
Dan Crawford was a British theatre producer and director best known as the founder and long-time artistic director of London’s influential fringe venue, the King’s Head Theatre.
-
B.
Greg Crawford
Greg Crawford is an American academic leader and physicist best known for serving as the president of Miami University in Ohio.
-
C.
Curtis Hixon
Curtis Hixon was a prominent Tampa, Florida mayor and civic leader whose contributions to the city led to major public landmarks being named in his honor.
-
D.
Ray Cusick
Ray Cusick was a British designer best known for creating the iconic look of the Daleks in the long-running science fiction television series Doctor Who.
-
E.
Chip Hardesty
Chip Hardesty is the fictional FBI agent protagonist of the 1959 film "The FBI Story," whose career dramatizes the Bureau’s history and major cases.
- 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: Ed Crenshaw Triple: [Publix, hasKeyPerson, Ed Crenshaw]
Generated description
Ed Crenshaw is an American business executive best known for serving as the CEO of Publix Super Markets.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ed Crenshaw Target entity description: Ed Crenshaw is an American business executive best known for serving as the CEO of Publix Super Markets.
-
A.
Dan Crawford
Dan Crawford was a British theatre producer and director best known as the founder and long-time artistic director of London’s influential fringe venue, the King’s Head Theatre.
-
B.
Greg Crawford
Greg Crawford is an American academic leader and physicist best known for serving as the president of Miami University in Ohio.
-
C.
Curtis Hixon
Curtis Hixon was a prominent Tampa, Florida mayor and civic leader whose contributions to the city led to major public landmarks being named in his honor.
-
D.
Ray Cusick
Ray Cusick was a British designer best known for creating the iconic look of the Daleks in the long-running science fiction television series Doctor Who.
-
E.
Chip Hardesty
Chip Hardesty is the fictional FBI agent protagonist of the 1959 film "The FBI Story," whose career dramatizes the Bureau’s history and major cases.
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63d3254548190828a5f7e9a851ef8 |
completed | April 20, 2026, 2:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07fdb0fdb081908e803fb558158374 |
completed | May 16, 2026, 5:16 a.m. |
| NEDg | Description generation | batch_6a07fe4573a48190919a59e466b34e35 |
completed | May 16, 2026, 5:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07fede18a08190a7a7a698d7598023 |
completed | May 16, 2026, 5:21 a.m. |
Created at: April 10, 2026, 1:41 p.m.