Triple
T14582459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roxanne Lee |
E342224
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Tom Stone |
E548688
|
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: Tom Stone | Statement: [Roxanne Lee, notableWork, Tom Stone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Stone Context triple: [Roxanne Lee, notableWork, Tom Stone]
-
A.
Tom Stone
chosen
Tom Stone is an American soccer coach best known for his work in women’s soccer, including collegiate and professional teams.
-
B.
Jeff Stone
Jeff Stone is the teenage son in the classic American sitcom "The Donna Reed Show," known for his wholesome, all-American boy persona.
-
C.
Jon Stone
Jon Stone was an American television producer, director, and writer best known as a key creative force behind the development and early success of the children's program "Sesame Street."
-
D.
Peter Stone
Peter Stone was an American screenwriter and playwright best known for crafting witty, sophisticated scripts for films such as "Charade" and the musical "1776."
-
E.
Peter Stone
Peter Stone is an American computer scientist known for his influential work in artificial intelligence and robotics, particularly in multiagent systems and robot soccer.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb41e71748190a1deacc819dd26d3 |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94ba235c81909e0408ccf2be9628 |
completed | May 8, 2026, 7:46 a.m. |
Created at: April 10, 2026, 1:24 a.m.