Triple
T13457933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Lassen |
E311281
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Peter Lassen |
E311281
|
NE FINISHED |
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: Peter Lassen | Statement: [Peter Lassen, name, Peter Lassen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Lassen Context triple: [Peter Lassen, name, Peter Lassen]
-
A.
Peter Lassen
chosen
Peter Lassen was a Danish-American frontiersman and guide known for pioneering overland emigrant routes to California during the mid-19th century.
-
B.
Fred T. Perris
Fred T. Perris was a 19th-century American railroad engineer and surveyor who played a key role in the development of rail infrastructure in Southern California.
-
C.
Leland Palmer
Leland Palmer is an American actress, singer, and dancer best known for her work in musical theatre and film during the 1960s and 1970s.
-
D.
Milton Van Dyke
Milton Van Dyke was an influential American fluid dynamicist and author known for his classic works on aerodynamics and fluid mechanics, including the widely used reference "An Album of Fluid Motion."
-
E.
John Lounsbery
John Lounsbery was an American animator and one of Disney’s famed "Nine Old Men," known for his influential work on many classic Disney animated films.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf0a75008190a508060c85f73604 |
completed | April 12, 2026, 2:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f739a001d08190ae5664c6670540e7 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:41 p.m.