Triple
T5741547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jim Halpert |
E126624
|
entity |
| Predicate | sibling |
P363
|
FINISHED |
| Object | Tom Halpert |
E543085
|
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: Tom Halpert | Statement: [Jim Halpert, sibling, Tom Halpert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Halpert Context triple: [Jim Halpert, sibling, Tom Halpert]
-
A.
David Puddy
David Puddy is a deadpan, dim-witted auto mechanic and Elaine Benes’s on-again, off-again boyfriend on the sitcom "Seinfeld," known for his monotone delivery and quirky obsessions.
-
B.
Halpert
chosen
Halpert is the surname of Jim Halpert, a central character from the American television series "The Office."
-
C.
Ted Baxter
Ted Baxter is a vain, bumbling, and egotistical TV news anchor who serves as a major comic figure on the classic sitcom "The Mary Tyler Moore Show."
-
D.
Maury Winetrobe
Maury Winetrobe is a film editor best known for his work on classic Hollywood productions such as "Pocketful of Miracles."
-
E.
John Winger
John Winger is the wisecracking, laid-back Army recruit played by Bill Murray in the 1981 comedy film "Stripes."
- 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_69c0083179548190b384b0bf3c08ca4d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0258382908190af8787feb1e5fbcd |
completed | March 22, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c097f655e881909f6944e9a9d27e6c |
completed | March 23, 2026, 1:31 a.m. |
Created at: March 22, 2026, 3:48 p.m.