Triple
T1407950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Smith |
E31738
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Matt Weitzman |
E78414
|
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: Matt Weitzman | Statement: [Steve Smith, creator, Matt Weitzman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Weitzman Context triple: [Steve Smith, creator, Matt Weitzman]
-
A.
Matt Weitzman
chosen
Matt Weitzman is an American television writer and producer best known as a co-creator and executive producer of the animated series "American Dad!"
-
B.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
C.
Nathan Grossman
Nathan Grossman is a Swedish documentary filmmaker best known for directing the climate activist portrait film "I Am Greta."
-
D.
Ryan Roslansky
Ryan Roslansky is the CEO of LinkedIn, known for leading the professional networking platform’s product and business strategy.
-
E.
Mark Rosenberg
Mark Rosenberg was an American film producer known for his work on notable movies of the 1980s and early 1990s.
- 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_69a49918e1f88190ba610f9dc8114578 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3bf7f0c8190aee96818de6ff4a5 |
completed | March 1, 2026, 10:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae3037944c8190b2ced5f5ca539260 |
completed | March 9, 2026, 2:28 a.m. |
Created at: March 1, 2026, 7:59 p.m.