Triple
T2665250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cedar Rapids |
E55618
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Tim Lippe
Tim Lippe is the naive, small-town insurance salesman who serves as the protagonist of the comedy film "Cedar Rapids."
|
E288494
|
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: Tim Lippe | Statement: [Cedar Rapids, mainCharacter, Tim Lippe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tim Lippe Context triple: [Cedar Rapids, mainCharacter, Tim Lippe]
-
A.
Tim Pilger
Tim Pilger is an individual notable enough to be specifically cited as a bearer of the surname Pilger.
-
B.
Rick Lieder
Rick Lieder is an American artist, illustrator, and photographer known for his book cover art and nature photography.
-
C.
Eric Wetzels
Eric Wetzels is a Dutch politician who serves as the chairperson of the People's Party for Freedom and Democracy (VVD).
-
D.
Michael Andlauer
Michael Andlauer is a Canadian businessman and sports executive best known as the owner of the NHL’s Ottawa Senators and a longtime figure in professional hockey ownership.
-
E.
Ted Szilva
Ted Szilva was a Canadian entrepreneur and community promoter best known for conceiving and helping create Sudbury’s iconic Big Nickel landmark.
- 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: Tim Lippe Triple: [Cedar Rapids, mainCharacter, Tim Lippe]
Generated description
Tim Lippe is the naive, small-town insurance salesman who serves as the protagonist of the comedy film "Cedar Rapids."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tim Lippe Target entity description: Tim Lippe is the naive, small-town insurance salesman who serves as the protagonist of the comedy film "Cedar Rapids."
-
A.
Tim Pilger
Tim Pilger is an individual notable enough to be specifically cited as a bearer of the surname Pilger.
-
B.
Rick Lieder
Rick Lieder is an American artist, illustrator, and photographer known for his book cover art and nature photography.
-
C.
Eric Wetzels
Eric Wetzels is a Dutch politician who serves as the chairperson of the People's Party for Freedom and Democracy (VVD).
-
D.
Michael Andlauer
Michael Andlauer is a Canadian businessman and sports executive best known as the owner of the NHL’s Ottawa Senators and a longtime figure in professional hockey ownership.
-
E.
Ted Szilva
Ted Szilva was a Canadian entrepreneur and community promoter best known for conceiving and helping create Sudbury’s iconic Big Nickel landmark.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd96ed2748190a4feae98199b459d |
completed | March 7, 2026, 7:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa058fdd08190a355fc8131cd6695 |
completed | March 10, 2026, 4:38 a.m. |
| NEDg | Description generation | batch_69afa15a8a94819095273a3ea37e2c73 |
completed | March 10, 2026, 4:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afa1e4ffb08190a6d96665ee566ea7 |
completed | March 10, 2026, 4:45 a.m. |
Created at: March 6, 2026, 9:54 p.m.