Triple
T23011773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PAX TV |
E572923
|
entity |
| Predicate | notableProgram |
P4
|
FINISHED |
| Object |
Doc
Doc is an American drama television series starring Billy Ray Cyrus as a compassionate small-town doctor who moves to New York City to work at a medical clinic.
|
E1566909
|
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: Doc | Statement: [PAX TV, notableProgram, Doc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doc Context triple: [PAX TV, notableProgram, Doc]
-
A.
Doc
Doc is one of the seven dwarfs in Disney's "Snow White and the Seven Dwarfs," characterized as their kindly, bearded leader who often fumbles his words.
-
B.
Doc
Doc is the wise, retired race car and town doctor from the animated film "Cars," who mentors Lightning McQueen.
-
C.
Doc
Doc is a wisecracking, bearded survivor and medic in the post-apocalyptic TV series "Z Nation," known for his laid-back demeanor and unexpected resourcefulness.
-
D.
Doc
Doc is a gentle, eccentric marine biologist in John Steinbeck’s novel "Cannery Row," known for his intelligence, compassion, and central role in the community’s life.
-
E.
Doc
Doc is the famous nickname of Doc Holliday, the American Old West gambler, gunfighter, and associate of Wyatt Earp.
- 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: Doc Triple: [PAX TV, notableProgram, Doc]
Generated description
Doc is an American drama television series starring Billy Ray Cyrus as a compassionate small-town doctor who moves to New York City to work at a medical clinic.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Doc Target entity description: Doc is an American drama television series starring Billy Ray Cyrus as a compassionate small-town doctor who moves to New York City to work at a medical clinic.
-
A.
Doc
Doc is a wisecracking, bearded survivor and medic in the post-apocalyptic TV series "Z Nation," known for his laid-back demeanor and unexpected resourcefulness.
-
B.
Doc
Doc is a key supporting character in the television series "Van Helsing," known for her medical expertise and complex moral evolution amid a post-apocalyptic vampire outbreak.
-
C.
Doc
Doc is the famous nickname of Doc Holliday, the American Old West gambler, gunfighter, and associate of Wyatt Earp.
-
D.
Doc
Doc is the wise, retired race car and town doctor from the animated film "Cars," who mentors Lightning McQueen.
-
E.
Doc
Doc is a gentle, eccentric marine biologist in John Steinbeck’s novel "Cannery Row," known for his intelligence, compassion, and central role in the community’s life.
- 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f183e202a481908d7a2f00a12229a0 |
completed | April 29, 2026, 4:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bf2a65a10819087b5954853a95c65 |
completed | May 19, 2026, 5:18 a.m. |
| NEDg | Description generation | batch_6a0bfbc5b19c8190aed3f1199410b996 |
completed | May 19, 2026, 5:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bfccedf2c8190833df43ea2c294ca |
completed | May 19, 2026, 6:01 a.m. |
Created at: April 17, 2026, 3:51 p.m.