Triple
T5513616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Infocom |
E144627
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Joel Berez
Joel Berez is an American businessman best known as a co-founder and early leader of the pioneering interactive fiction game company Infocom.
|
E534555
|
NE FINISHED |
How this triple was built (4 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, foundedBy, Joel Berez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joel Berez Context triple: [Infocom, foundedBy, Joel Berez]
-
A.
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.
-
B.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
-
C.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
-
D.
Michael Jablow
Michael Jablow is a film editor best known for his work on major Hollywood movies, including the baseball comedy-drama "A League of Their Own."
-
E.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Joel Berez Triple: [Infocom, foundedBy, Joel Berez]
Generated description
Joel Berez is an American businessman best known as a co-founder and early leader of the pioneering interactive fiction game company Infocom.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joel Berez Target entity description: Joel Berez is an American businessman best known as a co-founder and early leader of the pioneering interactive fiction game company Infocom.
-
A.
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.
-
B.
Max Zaritsky
Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
-
C.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
-
D.
Michael Jablow
Michael Jablow is a film editor best known for his work on major Hollywood movies, including the baseball comedy-drama "A League of Their Own."
-
E.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
- F. None of above. chosen
Provenance (5 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_69c008f77ff88190b0cd50ca207295d1 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f599d0881909ce86fcc45d4d920 |
completed | March 22, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04cc0735881909b7ea6909570a750 |
completed | March 22, 2026, 8:10 p.m. |
| NEDg | Description generation | batch_69c04e827bdc819086e01e7043400452 |
completed | March 22, 2026, 8:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c04f088a3c81909610f1a564960e0f |
completed | March 22, 2026, 8:20 p.m. |
Created at: March 22, 2026, 3:33 p.m.