Triple
T9320526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juliana Minsky |
E224241
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Minsky |
E98082
|
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: Minsky | Statement: [Juliana Minsky, familyName, Minsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Minsky Context triple: [Juliana Minsky, familyName, Minsky]
-
A.
Minsky
chosen
Minsky is a surname most notably associated with Marvin Minsky, a pioneering American cognitive scientist and co-founder of the field of artificial intelligence.
-
B.
Hyman Minsky
Hyman Minsky was an American economist best known for his financial instability hypothesis, which explains how periods of economic stability can lead to speculative excess and ultimately financial crises.
-
C.
Tobin
Tobin is the given name of Tobin Heath, an American professional soccer player and multiple-time FIFA Women's World Cup champion.
-
D.
Fama
Fama is the surname of Eugene Fama, a Nobel Prize–winning American economist renowned for his work on efficient markets and asset pricing.
-
E.
Henry Minsky
Henry Minsky is the son of artificial intelligence pioneer Marvin Minsky and is known as a software engineer and technologist.
- 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_69ca8426d48481909596360f7791c7dd |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd358dcb4c81909e00bfb58a6dda3f |
completed | April 1, 2026, 3:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c7cc71e48190afdc3f1120ce5e02 |
completed | April 4, 2026, 8:11 a.m. |
Created at: March 30, 2026, 7:38 p.m.