Triple
T16053342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweet Home Alabama |
E389409
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Robin Russell |
E715478
|
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: Robin Russell | Statement: [Sweet Home Alabama, editedBy, Robin Russell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Robin Russell Context triple: [Sweet Home Alabama, editedBy, Robin Russell]
-
A.
Robin Russell
chosen
Robin Russell is an editor known for her work on the film "Life-Size."
-
B.
Deborah Rush
Deborah Rush is an American actress known for her character roles in film, television, and theater, including appearances in comedies and independent productions.
-
C.
Jill Eikenberry
Jill Eikenberry is an American actress best known for her Emmy-nominated role as attorney Ann Kelsey on the television series "L.A. Law."
-
D.
Lee Russo
Lee Russo is known as the spouse of American television host and film critic Ben Mankiewicz.
-
E.
Tyne Daly
Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1836365688190b182f29bbb66127e |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00456d5e74819080c838468ec015ef |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 4:56 a.m.