Triple
T7616944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abelson |
E172384
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Hal Abelson |
E32009
|
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: Hal Abelson | Statement: [Abelson, hasNotableBearer, Hal Abelson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hal Abelson Context triple: [Abelson, hasNotableBearer, Hal Abelson]
-
A.
Hal Abelson
chosen
Hal Abelson is an American computer scientist and MIT professor known for his pioneering work in computer science education, open knowledge, and software freedom.
-
B.
Andrew G. Myers
Andrew G. Myers is an American organic chemist renowned for his contributions to complex molecule synthesis and medicinal chemistry.
-
C.
Richard P. Gabriel
Richard P. Gabriel is a computer scientist and writer best known for his work on Lisp, software patterns, and his influential essay "Worse Is Better."
-
D.
Jack Schwartz
Jack Schwartz was an American mathematician and computer scientist known for his contributions to programming languages, parallel computing, and the development of the SETL language.
-
E.
Dan Goldberg
Dan Goldberg is a film producer best known for his work on major Hollywood comedies, including the hit movie "The Hangover."
- 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_69c699506b308190826894dab1d9ea86 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fa46d95081909c01d1432585ab2a |
completed | March 27, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c868714d7c8190aae66a3dd4e6214b |
completed | March 28, 2026, 11:46 p.m. |
Created at: March 27, 2026, 3:55 p.m.