Triple
T9746707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Seaton |
E236328
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | George Seaton |
E236328
|
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: George Seaton | Statement: [George Seaton, name, George Seaton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George Seaton Context triple: [George Seaton, name, George Seaton]
-
A.
George Seaton
chosen
George Seaton was an American screenwriter, director, and producer best known for films such as "Miracle on 34th Street" and "Airport."
-
B.
Thomas Mayne
Thomas Mayne was an Australian food scientist best known for creating the chocolate malted milk drink Milo in the 1930s.
-
C.
John Seaton
John Seaton is a fictional character from the 1978 British-American sports drama film "International Velvet."
-
D.
Walter Boyd
Walter Boyd is a former Jamaican international footballer best known for his prolific goal-scoring and charismatic playing style as a forward in the 1990s and early 2000s.
-
E.
Joseph McHardy
Joseph McHardy is a musician and choral director known for his work with prominent British church and cathedral choirs.
- 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_69ca84d3e24481908a476e2231123cf9 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f677830819096d388b9c798ecd5 |
completed | April 1, 2026, 10:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eac24eb0819083fa42f9ada99f6a |
completed | April 5, 2026, 4:53 a.m. |
Created at: March 30, 2026, 8:23 p.m.