Triple
T16252348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweet Thursday |
E394540
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Doc |
E394535
|
NE FINISHED |
How this triple was built (2 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: [Sweet Thursday, mainCharacter, Doc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doc Context triple: [Sweet Thursday, mainCharacter, 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 the widely used nickname of Glenn "Doc" Rivers, a former NBA player and championship-winning head coach.
-
E.
Doc
chosen
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.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24597b74481908fdb8175628a57a1 |
completed | April 17, 2026, 2:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f8ca4508190a8ed7a9159dfb551 |
completed | May 10, 2026, 6:02 a.m. |
Created at: April 10, 2026, 5:04 a.m.