Triple
T21659491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Infocom |
E534555
|
entity |
| Predicate | hasFounder |
P104
|
FINISHED |
| Object | Joel Berez |
—
|
NE NERFINISHED |
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: Joel Berez | Statement: [Infocom, hasFounder, Joel Berez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joel Berez Context triple: [Infocom, hasFounder, Joel Berez]
-
A.
Joel Berez
chosen
Joel Berez is an American businessman best known as a co-founder and early leader of the pioneering interactive fiction game company Infocom.
-
B.
Joel Benenson
Joel Benenson is an American pollster and political strategist best known as the chief pollster for Barack Obama’s presidential campaigns.
-
C.
Joel Bergman
Joel Bergman was an American architect best known for designing iconic, large-scale casino resorts in Las Vegas.
-
D.
Joel Stillerman
Joel Stillerman is a television and film executive and producer known for his influential programming roles at networks like AMC and Hulu.
-
E.
Joel Stransky
Joel Stransky is a former South African rugby union fly-half best known for kicking the winning drop goal in the 1995 Rugby World Cup final.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c467e1f48190af2650b19175abc4 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef6c06844c81909b9c91e02fa4e6e1 |
completed | April 27, 2026, 2 p.m. |
Created at: April 16, 2026, 6:36 p.m.