Triple
T10419897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lammers |
E245617
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Rick Lammers
Rick Lammers is an individual notable enough to be specifically cited as a bearer of the surname Lammers.
|
E952606
|
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: Rick Lammers | Statement: [Lammers, hasNotableBearer, Rick Lammers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rick Lammers Context triple: [Lammers, hasNotableBearer, Rick Lammers]
-
A.
Tim Lammers
Tim Lammers is a film critic and entertainment journalist known for his movie reviews and celebrity interviews across various media outlets.
-
B.
Matt Lammers
Matt Lammers is an individual notable enough to be specifically referenced as a bearer of the surname Lammers, though detailed public information about him is limited.
-
C.
Greg Wuliger
Greg Wuliger is Chris Rock’s loyal, nerdy best friend in the sitcom "Everybody Hates Chris," known for his quirky personality and unwavering support.
-
D.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
-
E.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
- 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: Rick Lammers Triple: [Lammers, hasNotableBearer, Rick Lammers]
Generated description
Rick Lammers is an individual notable enough to be specifically cited as a bearer of the surname Lammers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rick Lammers Target entity description: Rick Lammers is an individual notable enough to be specifically cited as a bearer of the surname Lammers.
-
A.
Tim Lammers
Tim Lammers is a film critic and entertainment journalist known for his movie reviews and celebrity interviews across various media outlets.
-
B.
Matt Lammers
Matt Lammers is an individual notable enough to be specifically referenced as a bearer of the surname Lammers, though detailed public information about him is limited.
-
C.
Greg Wuliger
Greg Wuliger is Chris Rock’s loyal, nerdy best friend in the sitcom "Everybody Hates Chris," known for his quirky personality and unwavering support.
-
D.
Kevin Nolting
Kevin Nolting is an American film editor best known for his work on Pixar animated features, including the Academy Award-winning film "Up."
-
E.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea2aa7848190a7091ee71722fcc6 |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f416ab88e48190b3089caab7987191 |
completed | May 1, 2026, 2:57 a.m. |
| NEDg | Description generation | batch_69f41f16f43c81909f5d36e8b4b0b9c3 |
completed | May 1, 2026, 3:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f4225a4b5c8190958aaddbd10035b1 |
completed | May 1, 2026, 3:47 a.m. |
Created at: April 6, 2026, 12:11 p.m.