Triple
T7024016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pam Bryant |
E162897
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Pam Bryant |
E162897
|
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 Bryant | Statement: [Pam Bryant, name, Pam Bryant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pam Bryant Context triple: [Pam Bryant, name, Pam Bryant]
-
A.
Pam Bryant
chosen
Pam Bryant is an American woman best known as the mother of the late NBA superstar Kobe Bryant.
-
B.
Pattie Mallette
Pattie Mallette is a Canadian author and film producer best known as the mother of pop singer Justin Bieber.
-
C.
Wendy Harris
Wendy Harris is a character known for being the mother figure in the context of the work in which she appears.
-
D.
Tawny Newsome
Tawny Newsome is an American actress, comedian, and musician best known for her comedic roles and voice work, including starring in the animated series Star Trek: Lower Decks.
-
E.
Tyler Summitt
Tyler Summitt is an American former college basketball coach and the son of legendary Tennessee women's coach Pat Summitt.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1fa043c81909c900e394a5972f9 |
completed | March 27, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c77581e2a88190ad2ec9855772c6a5 |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:35 p.m.