Triple
T3759931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Doug Ross |
E82135
|
entity |
| Predicate | hasColleague |
P398
|
FINISHED |
| Object |
Mark Greene
Mark Greene is a central fictional emergency physician and one of the original main characters on the television series "ER."
|
E385760
|
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: Mark Greene | Statement: [Dr. Doug Ross, hasColleague, Mark Greene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Greene Context triple: [Dr. Doug Ross, hasColleague, Mark Greene]
-
A.
Scott Green
Scott Green is a former National Football League official best known for serving as a referee in multiple Super Bowls.
-
B.
Bruce Green
Bruce Green is a film editor known for his work on feature films including the 1995 drama "The Basketball Diaries."
-
C.
Jeff Gourson
Jeff Gourson is a film editor known for his work on movies such as the comedy "White Chicks."
-
D.
Brant Daugherty
Brant Daugherty is an American actor known for his roles in television series like "Pretty Little Liars" and films including the "Fifty Shades" franchise.
-
E.
Ken Schretzmann
Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
- 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: Mark Greene Triple: [Dr. Doug Ross, hasColleague, Mark Greene]
Generated description
Mark Greene is a central fictional emergency physician and one of the original main characters on the television series "ER."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Greene Target entity description: Mark Greene is a central fictional emergency physician and one of the original main characters on the television series "ER."
-
A.
Scott Green
Scott Green is a former National Football League official best known for serving as a referee in multiple Super Bowls.
-
B.
Bruce Green
Bruce Green is a film editor known for his work on feature films including the 1995 drama "The Basketball Diaries."
-
C.
Jeff Gourson
Jeff Gourson is a film editor known for his work on movies such as the comedy "White Chicks."
-
D.
Brant Daugherty
Brant Daugherty is an American actor known for his roles in television series like "Pretty Little Liars" and films including the "Fifty Shades" franchise.
-
E.
Ken Schretzmann
Ken Schretzmann is a film editor known for his work on major animated features, including Guillermo del Toro's stop-motion adaptation of Pinocchio.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc3d3f48190974cec104080949f |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e5172abc81909cfa709ea866dc57 |
completed | March 14, 2026, 4:33 a.m. |
| NEDg | Description generation | batch_69b4e65f868c819099b4fbf129773e6d |
completed | March 14, 2026, 4:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4e6bc95cc8190852b833e111cd889 |
completed | March 14, 2026, 4:40 a.m. |
Created at: March 8, 2026, 3:35 p.m.