Triple
T9853862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Witness |
E239535
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Samuel Lapp
Samuel Lapp is a young Amish boy in the film "Witness," whose accidental observation of a murder drives the story’s central conflict.
|
E825057
|
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: Samuel Lapp | Statement: [Witness, hasCharacter, Samuel Lapp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samuel Lapp Context triple: [Witness, hasCharacter, Samuel Lapp]
-
A.
Samuel Diescher
Samuel Diescher was a prominent 19th-century civil and mechanical engineer known for designing several American inclines and industrial structures, particularly in Pittsburgh.
-
B.
Samuel Baum
Samuel Baum is a television writer and producer best known for creating the crime drama series "Lie to Me."
-
C.
Samuel Blum
Samuel Blum is a relatively obscure individual whose specific notability is not clearly established from the given information.
-
D.
Samuel Joseph
Samuel Joseph is the son of British Conservative politician and former Education Secretary Keith Joseph.
-
E.
Samuel Marks
Samuel Marks is a relatively obscure individual whose specific public notability or distinguishing achievements are not clearly established from the given information.
- 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: Samuel Lapp Triple: [Witness, hasCharacter, Samuel Lapp]
Generated description
Samuel Lapp is a young Amish boy in the film "Witness," whose accidental observation of a murder drives the story’s central conflict.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Samuel Lapp Target entity description: Samuel Lapp is a young Amish boy in the film "Witness," whose accidental observation of a murder drives the story’s central conflict.
-
A.
Samuel Diescher
Samuel Diescher was a prominent 19th-century civil and mechanical engineer known for designing several American inclines and industrial structures, particularly in Pittsburgh.
-
B.
Samuel Baum
Samuel Baum is a television writer and producer best known for creating the crime drama series "Lie to Me."
-
C.
Samuel Blum
Samuel Blum is a relatively obscure individual whose specific notability is not clearly established from the given information.
-
D.
Samuel Joseph
Samuel Joseph is the son of British Conservative politician and former Education Secretary Keith Joseph.
-
E.
Samuel Marks
Samuel Marks is a relatively obscure individual whose specific public notability or distinguishing achievements are not clearly established from the given information.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb376d32c819089381cf6ed83629d |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5f21a04819099f23ede55ec3417 |
completed | April 5, 2026, 3:24 a.m. |
| NEDg | Description generation | batch_69d1d7a6a87c81908dcd79c776bb19a1 |
completed | April 5, 2026, 3:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1d82007088190ac372c67a6760e65 |
completed | April 5, 2026, 3:33 a.m. |
Created at: March 30, 2026, 8:34 p.m.