Triple
T13283838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atypical |
E316388
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Sam Gardner |
E1030419
|
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: Sam Gardner | Statement: [Atypical, character, Sam Gardner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sam Gardner Context triple: [Atypical, character, Sam Gardner]
-
A.
Sam Gardner
chosen
Sam Gardner is the socially awkward, autism-spectrum teenager at the heart of the Netflix dramedy "Atypical," whose journey toward independence and self-discovery drives the series.
-
B.
Jimmy Gardner
Jimmy Gardner was an early 20th-century Canadian ice hockey player, coach, and executive who played a key role in organizing professional hockey and shaping the sport’s development in North America.
-
C.
Nathan Gardner
Nathan Gardner is an educational administrator who serves as a school principal.
-
D.
Nathan Gardner
Nathan Gardner is a person known primarily as a relative of Susan Gardner.
-
E.
Will Gardner
Will Gardner is a charismatic and ambitious lawyer and name partner at the Chicago law firm Lockhart/Gardner in the television drama "The Good Wife."
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99047531c819087aa6406de1ddc82 |
completed | April 11, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716d26a548190be15872154c9a942 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:27 p.m.