Triple
T5091942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Total |
E114771
|
entity |
| Predicate | member |
P10
|
FINISHED |
| Object | Pamela Long |
E302068
|
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: Pamela Long | Statement: [Total, member, Pamela Long]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pamela Long Context triple: [Total, member, Pamela Long]
-
A.
Pam Long
chosen
Pam Long is an American R&B singer best known for her work with the group Total and her featured vocals on various hip-hop and R&B tracks.
-
B.
Pamela Frank
Pamela Frank is the second wife of singer and civil rights activist Harry Belafonte, known primarily for her long-term marriage to the entertainer.
-
C.
Pamela Frank
Pamela Frank is an acclaimed American violinist renowned for her expressive performances and influential teaching career.
-
D.
Pamela Franklin
Pamela Franklin is a British actress best known for her work as a child and young adult in 1960s and 1970s films and television, particularly in psychological horror and drama.
-
E.
Pamela Brown
Pamela Brown was a British stage and film actress known for her intense character roles in mid-20th-century cinema and theatre.
- 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_69bd443e941881908eb4e8c685b6f656 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7541b2bc8190b58c2a23733b7825 |
completed | March 20, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5187dfe008190ac60e042527e55b3 |
completed | March 26, 2026, 11:29 a.m. |
Created at: March 20, 2026, 1:40 p.m.