Triple
T6656287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Gardner |
E150952
|
entity |
| Predicate | fatherIs |
P1908
|
FINISHED |
| Object | Nathan Gardner |
E608708
|
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: Nathan Gardner | Statement: [Susan Gardner, fatherIs, Nathan Gardner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nathan Gardner Context triple: [Susan Gardner, fatherIs, Nathan Gardner]
-
A.
Nathan Gardner
Nathan Gardner is an educational administrator who serves as a school principal.
-
B.
Nathan Gardner
chosen
Nathan Gardner is a person known primarily as a relative of Susan Gardner.
-
C.
Nathan Gamble
Nathan Gamble is an American actor best known for his childhood roles in films such as "Babel" and the "Dolphin Tale" series.
-
D.
Nathan Bateman
Nathan Bateman is the reclusive, manipulative tech billionaire and AI creator in the science fiction film "Ex Machina."
-
E.
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.
- 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b06dbbf88190b39564a688c25a24 |
completed | March 27, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f79817f08190a9e499f09b25e67a |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:01 p.m.