Triple
T21898528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Lapp |
E540746
|
entity |
| Predicate | guardianOf |
P1040
|
FINISHED |
| Object | Samuel Lapp |
—
|
NE NERFINISHED |
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: Samuel Lapp | Statement: [Rachel Lapp, guardianOf, Samuel Lapp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samuel Lapp Context triple: [Rachel Lapp, guardianOf, Samuel Lapp]
-
A.
Samuel Lapp
chosen
Samuel Lapp is a young Amish boy in the film "Witness," whose accidental observation of a murder drives the story’s central conflict.
-
B.
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.
-
C.
Samuel Baum
Samuel Baum is a television writer and producer best known for creating the crime drama series "Lie to Me."
-
D.
Samuel Blum
Samuel Blum is a relatively obscure individual whose specific notability is not clearly established from the given information.
-
E.
Samuel Joseph
Samuel Joseph is the son of British Conservative politician and former Education Secretary Keith Joseph.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fc8c2108190b55ff1ba3badc9fb |
completed | April 28, 2026, 8:59 p.m. |
Created at: April 16, 2026, 7:07 p.m.