Triple
T8904773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The State |
E212026
|
entity |
| Predicate | member |
P10
|
FINISHED |
| Object | Thomas Lennon |
E138740
|
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: Thomas Lennon | Statement: [The State, member, Thomas Lennon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thomas Lennon Context triple: [The State, member, Thomas Lennon]
-
A.
Thomas Lennon
chosen
Thomas Lennon is an American actor, comedian, and screenwriter known for co-writing hit comedy films such as the "Night at the Museum" series and for his role on the TV show "Reno 911!".
-
B.
Lloyd Nolan
Lloyd Nolan was an American film and television actor known for his versatile character roles in dramas, crime films, and later in popular TV series.
-
C.
Peter O’Neill
Peter O’Neill is a Papua New Guinean politician who served as Prime Minister of Papua New Guinea from 2011 to 2019.
-
D.
Al Higgins
Al Higgins is a television producer best known for his work on acclaimed comedy series, including serving as an executive producer on Netflix’s "The Kominsky Method."
-
E.
John Lacey
John Lacey is the neurotic, recently divorced middle-aged father who serves as the central character in the sitcom "Dear John."
- 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_69ca839255248190b43984294abd92ae |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc64c2509881908fb692522d348e96 |
completed | April 1, 2026, 12:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfba2cb5e48190813e9c08198149b0 |
completed | April 3, 2026, 1:01 p.m. |
Created at: March 30, 2026, 6:55 p.m.