Triple
T19627808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 3000 Miles to Graceland |
E471183
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Demian Lichtenstein |
—
|
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: Demian Lichtenstein | Statement: [3000 Miles to Graceland, writer, Demian Lichtenstein]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Demian Lichtenstein Context triple: [3000 Miles to Graceland, writer, Demian Lichtenstein]
-
A.
Demian Lichtenstein
chosen
Demian Lichtenstein is an American film director, producer, and writer best known for his work on action and crime films in Hollywood.
-
B.
Mitchell Lichtenstein
Mitchell Lichtenstein is an American actor and filmmaker known for roles in independent cinema and for directing the cult horror-comedy film "Teeth."
-
C.
Michael Lichtefeld
Michael Lichtefeld is a theater choreographer best known for his work on major Broadway musicals, including the 1991 production of *The Secret Garden*.
-
D.
Uriel Frisch
Uriel Frisch is a French physicist and mathematician renowned for his contributions to fluid dynamics and turbulence theory.
-
E.
Alexander Saeltzer
Alexander Saeltzer was a 19th-century German-American architect known for designing significant public and institutional buildings in New York City.
- 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e641007e5881908da78e50aa36f340 |
completed | April 20, 2026, 3:06 p.m. |
Created at: April 10, 2026, 1:44 p.m.