Triple
T10750138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Love Hangover |
E253550
|
entity |
| Predicate | songwriter |
P1141
|
FINISHED |
| Object | Pam Sawyer |
E781485
|
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: Pam Sawyer | Statement: [Love Hangover, songwriter, Pam Sawyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pam Sawyer Context triple: [Love Hangover, songwriter, Pam Sawyer]
-
A.
Pam Sawyer
chosen
Pam Sawyer is an American songwriter best known for co-writing numerous Motown hits in the 1960s and 1970s.
-
B.
Pam Ferris
Pam Ferris is a British actress known for her character roles in film and television, including memorable performances in "Matilda," "Call the Midwife," and "Harry Potter and the Prisoner of Azkaban."
-
C.
Pam Baker
Pam Baker is best known as the longtime wife of English rock and blues singer Joe Cocker, with whom she shared a ranch in Colorado and managed aspects of his personal and professional life.
-
D.
Pam Williams
Pam Williams is a film producer best known for her work on the critically acclaimed historical drama "The Butler."
-
E.
Pam Sheyne
Pam Sheyne is a British songwriter and producer best known for co-writing Christina Aguilera’s hit single "Genie in a Bottle."
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d71dbfe5f481908eed42328447b158 |
completed | April 9, 2026, 3:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2d6878a54819080050011e718c4e8 |
completed | April 18, 2026, 12:55 a.m. |
Created at: April 8, 2026, 9:15 p.m.