Triple
T21355032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dina Powell |
E526595
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Powell |
—
|
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: Powell | Statement: [Dina Powell, familyName, Powell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Powell Context triple: [Dina Powell, familyName, Powell]
-
A.
Powell
chosen
Powell is a common English-language surname borne by numerous notable figures in politics, finance, sports, and the arts.
-
B.
Powell and Donovan
Powell and Donovan are a pair of fictional robot technicians featured in Isaac Asimov’s science fiction stories, often involved in troubleshooting complex robotic and ethical dilemmas.
-
C.
Vance
Vance is a surname most prominently associated with Emmy and Tony Award–winning American actor Courtney B. Vance.
-
D.
Vance
Vance is a central character in the American television sitcom "Perfect Couples," known for his volatile yet comedic romantic relationship dynamics.
-
E.
Vance
Vance is a small town in Alabama, United States, known primarily for hosting the Mercedes-Benz U.S. International manufacturing plant.
- 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_69e0b51cd5cc81909ac1187971e8a8ad |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8af9be3a48190aa8e6e9a5b812981 |
completed | April 22, 2026, 11:23 a.m. |
Created at: April 16, 2026, 5:06 p.m.