Triple
T13616825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | As Good as It Gets |
E325336
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Dr. Green
Dr. Green is a minor supporting character in the 1997 romantic comedy-drama film "As Good as It Gets."
|
E1051586
|
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: Dr. Green | Statement: [As Good as It Gets, character, Dr. Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dr. Green Context triple: [As Good as It Gets, character, Dr. Green]
-
A.
Dr. Smith
Dr. Smith is a manipulative and enigmatic antagonist in the 2018 reboot of "Lost in Space," whose schemes and shifting loyalties create constant tension for the Robinson family.
-
B.
Dr. Hill
Dr. Hill is a medical professional who provided healthcare treatment to Joshua Washington.
-
C.
Doc Green
Doc Green is a hyper-intelligent, emotionally colder incarnation of the Hulk persona that emerges from Bruce Banner after he is exposed to enhanced gamma radiation.
-
D.
Dr. Harper
Dr. Harper is a character in the horror film "The Boogeyman," serving as a key figure in the story’s unfolding supernatural terror.
-
E.
Dr. Robinson
Dr. Robinson is a minor but pivotal character in Mark Twain's novel "The Adventures of Tom Sawyer," whose murder in the graveyard sets off a central mystery in the story.
- 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: Dr. Green Triple: [As Good as It Gets, character, Dr. Green]
Generated description
Dr. Green is a minor supporting character in the 1997 romantic comedy-drama film "As Good as It Gets."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dr. Green Target entity description: Dr. Green is a minor supporting character in the 1997 romantic comedy-drama film "As Good as It Gets."
-
A.
Dr. Smith
Dr. Smith is a manipulative and enigmatic antagonist in the 2018 reboot of "Lost in Space," whose schemes and shifting loyalties create constant tension for the Robinson family.
-
B.
Dr. Hill
Dr. Hill is a medical professional who provided healthcare treatment to Joshua Washington.
-
C.
Doc Green
Doc Green is a hyper-intelligent, emotionally colder incarnation of the Hulk persona that emerges from Bruce Banner after he is exposed to enhanced gamma radiation.
-
D.
Dr. Harper
Dr. Harper is a character in the horror film "The Boogeyman," serving as a key figure in the story’s unfolding supernatural terror.
-
E.
Dr. Robinson
Dr. Robinson is a minor but pivotal character in Mark Twain's novel "The Adventures of Tom Sawyer," whose murder in the graveyard sets off a central mystery in the story.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0ad0a7c81909c7972187202db96 |
completed | April 12, 2026, 2:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f9ecc2881909f71d71e056f9459 |
completed | May 3, 2026, 5:02 p.m. |
| NEDg | Description generation | batch_69f782c41634819096b0939c6c917259 |
completed | May 3, 2026, 5:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f78366eca88190be313c3a1b1e23ff |
completed | May 3, 2026, 5:18 p.m. |
Created at: April 9, 2026, 9:50 p.m.