Triple
T9579176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Christopher Turk |
E231123
|
entity |
| Predicate | closeColleague |
P11349
|
FINISHED |
| Object | Bob Kelso |
E828190
|
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: Bob Kelso | Statement: [Dr. Christopher Turk, closeColleague, Bob Kelso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bob Kelso Context triple: [Dr. Christopher Turk, closeColleague, Bob Kelso]
-
A.
Bob Kelso
chosen
Bob Kelso is a fictional, often sarcastic and bureaucratic chief of medicine on the television series "Scrubs."
-
B.
Don Dodson
Don Dodson is an individual whose name is associated with or referenced by the term "Dodson."
-
C.
Karl Pitterson
Karl Pitterson is a Jamaican record producer and audio engineer best known for his work on classic reggae and dub recordings in the 1970s and 1980s.
-
D.
Thomas Kinnear
Thomas Kinnear is a fictional Canadian gentleman and murder victim in Margaret Atwood’s novel "Alias Grace," whose death is central to the story’s mystery.
-
E.
Ted Cheesman
Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99aece1081908287e03106de020f |
completed | April 1, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20cbe7fb88190a945870540d4c973 |
completed | April 5, 2026, 7:18 a.m. |
Created at: March 30, 2026, 8:05 p.m.