Triple
T16097606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Doctors |
E390526
|
entity |
| Predicate | originalPanelist |
P121889
|
FINISHED |
| Object |
Andrew Ordon
Andrew Ordon is an American plastic surgeon and television personality best known as one of the co-hosts of the medical talk show "The Doctors."
|
E1194524
|
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: Andrew Ordon | Statement: [The Doctors, originalPanelist, Andrew Ordon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Andrew Ordon Context triple: [The Doctors, originalPanelist, Andrew Ordon]
-
A.
Eric Orsborn
Eric Orsborn is an American local government leader serving as the mayor of Buckeye, Arizona.
-
B.
Jon Orwant
Jon Orwant is a computer scientist and author best known for his influential work in the Perl programming community and contributions to technical publishing.
-
C.
John Orloff
John Orloff is an American screenwriter known for his work on films such as "A Mighty Heart" and contributions to acclaimed television projects.
-
D.
Mark Orr
Mark Orr is a collegiate sports administrator who serves as the athletic director for the Sacramento State Hornets.
-
E.
Michael D’Orso
Michael D’Orso is an American author and journalist known for co-writing influential nonfiction books, often chronicling social justice movements and notable public figures.
- 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: Andrew Ordon Triple: [The Doctors, originalPanelist, Andrew Ordon]
Generated description
Andrew Ordon is an American plastic surgeon and television personality best known as one of the co-hosts of the medical talk show "The Doctors."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Andrew Ordon Target entity description: Andrew Ordon is an American plastic surgeon and television personality best known as one of the co-hosts of the medical talk show "The Doctors."
-
A.
Eric Orsborn
Eric Orsborn is an American local government leader serving as the mayor of Buckeye, Arizona.
-
B.
Jon Orwant
Jon Orwant is a computer scientist and author best known for his influential work in the Perl programming community and contributions to technical publishing.
-
C.
John Orloff
John Orloff is an American screenwriter known for his work on films such as "A Mighty Heart" and contributions to acclaimed television projects.
-
D.
Mark Orr
Mark Orr is a collegiate sports administrator who serves as the athletic director for the Sacramento State Hornets.
-
E.
Michael D’Orso
Michael D’Orso is an American author and journalist known for co-writing influential nonfiction books, often chronicling social justice movements and notable public figures.
- 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21a00f6808190a60939ef7ce727a7 |
completed | April 17, 2026, 11:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeb9b3e708190be822f7ed588c9da |
completed | May 10, 2026, 2:21 a.m. |
| NEDg | Description generation | batch_69ffec7d0b188190805a471ed3a97eb5 |
completed | May 10, 2026, 2:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffed254d8c81909e0d86621c7792cb |
completed | May 10, 2026, 2:27 a.m. |
Created at: April 10, 2026, 4:59 a.m.