Triple
T7566984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jane Grierson |
E178939
|
entity |
| Predicate | isConnectedTo |
P845
|
FINISHED |
| Object | Fred Tate |
E189948
|
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: Fred Tate | Statement: [Jane Grierson, isConnectedTo, Fred Tate]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fred Tate Context triple: [Jane Grierson, isConnectedTo, Fred Tate]
-
A.
Fred Tate
chosen
Fred Tate is a child prodigy whose extraordinary intellectual abilities and emotional struggles are central to the drama film "Little Man Tate."
-
B.
Steuart Pittman
Steuart Pittman is a Maryland politician who serves as the county executive of Anne Arundel County, focusing on issues such as responsible development, environmental protection, and public services.
-
C.
Bob Hilliard
Bob Hilliard was an American lyricist known for writing popular songs for films and Broadway during the mid-20th century.
-
D.
Forrest Tucker
Forrest Tucker was an American actor best known for his roles in Westerns and classic films and later for his television work, including the sitcom "F Troop."
-
E.
Glen Tullman
Glen Tullman is an American healthcare technology entrepreneur and executive best known for leading and building major digital health companies, including Allscripts.
- 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_69c69f2f80288190b95cceb4da92ab2b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f91c717881909c88901cc3b101c7 |
completed | March 27, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c870780d24819095196cd3a22bec5a |
completed | March 29, 2026, 12:21 a.m. |
Created at: March 27, 2026, 3:50 p.m.