Triple
T10419891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lammers |
E245617
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
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.
|
E952066
|
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: Matt Lammers | Statement: [Lammers, hasNotableBearer, Matt Lammers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Lammers Context triple: [Lammers, hasNotableBearer, Matt 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 Luber
Matt Luber is a film producer best known for his work on the action-thriller movie "Into the Blue."
-
C.
Ryan Lomberg
Ryan Lomberg is a Canadian professional ice hockey forward known for his energetic, physical style of play in the NHL.
-
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.
Chris Bilheimer
Chris Bilheimer is an American graphic designer best known for creating album artwork for bands such as Green Day and R.E.M.
- 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: Matt Lammers Triple: [Lammers, hasNotableBearer, Matt Lammers]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matt Lammers Target entity description: 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.
-
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 Luber
Matt Luber is a film producer best known for his work on the action-thriller movie "Into the Blue."
-
C.
Ryan Lomberg
Ryan Lomberg is a Canadian professional ice hockey forward known for his energetic, physical style of play in the NHL.
-
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.
Chris Bilheimer
Chris Bilheimer is an American graphic designer best known for creating album artwork for bands such as Green Day and R.E.M.
- 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_69f2802d74f081909c7af34bf266ae01 |
completed | April 29, 2026, 10:03 p.m. |
| NEDg | Description generation | batch_69f28b7db84c8190b4c2b22a7465cf97 |
completed | April 29, 2026, 10:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f2a0e8861c8190a9ac8e0f488f4a95 |
completed | April 30, 2026, 12:23 a.m. |
Created at: April 6, 2026, 12:11 p.m.