Triple
T18044098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugene Fama |
E431727
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Fama |
—
|
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: Fama | Statement: [Eugene Fama, familyName, Fama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fama Context triple: [Eugene Fama, familyName, Fama]
-
A.
Fama
chosen
Fama is the surname of Eugene Fama, a Nobel Prize–winning American economist renowned for his work on efficient markets and asset pricing.
-
B.
Sedol
Sedol is the given name of Lee Sedol, the renowned South Korean professional Go player known for his historic matches against AI.
-
C.
Famo
Famo is a traditional Basotho music genre known for its accordion-driven melodies, poetic lyrics, and strong cultural and social commentary in Lesotho and surrounding regions.
-
D.
Tobin
Tobin is the given name of Tobin Heath, an American professional soccer player and multiple-time FIFA Women's World Cup champion.
-
E.
Farris
Farris is a surname most notably associated with Christine King Farris, an American educator, author, and the elder sister of Martin Luther King Jr.
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4bff13f488190993445769551c9c2 |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 10:25 a.m.