Triple
T19559282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristen Maloney |
E489399
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Kristen Maloney |
—
|
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: Kristen Maloney | Statement: [Kristen Maloney, name, Kristen Maloney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristen Maloney Context triple: [Kristen Maloney, name, Kristen Maloney]
-
A.
Kristen Maloney
chosen
Kristen Maloney is an American artistic gymnast and Olympic medalist who competed for the U.S. national team and later starred as a collegiate gymnast for UCLA.
-
B.
Kristen Buckley
Kristen Buckley is an American screenwriter best known for co-writing the hit romantic comedy film "How to Lose a Guy in 10 Days."
-
C.
Kirsten Corley
Kirsten Corley is an American former model and real estate agent best known as the wife of hip-hop artist Chance the Rapper.
-
D.
Kristi Bonnett
Kristi Bonnett is known as the daughter of the late NASCAR Cup Series driver Neil Bonnett.
-
E.
Kristen Scott
Kristen Scott is a reality television personality best known for appearing as a cast member on the VH1 series "Basketball Wives."
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63f723d5081909553a4363b579a6b |
completed | April 20, 2026, 3 p.m. |
Created at: April 10, 2026, 1:42 p.m.