Triple
T17928874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Debra Jennifer Stern |
E448273
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Debra |
—
|
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: Debra | Statement: [Debra Jennifer Stern, givenName, Debra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Debra Context triple: [Debra Jennifer Stern, givenName, Debra]
-
A.
Debra
Debra is a central character in Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," set in a futuristic, reputation-based society at Disney World.
-
B.
Debra
chosen
Debra is a feminine given name commonly used in English-speaking countries, derived from the Hebrew name Deborah meaning "bee."
-
C.
Debra
Debra is a troubled, introspective teenage employee at the independent record store in the 1995 cult film "Empire Records."
-
D.
Debra
"Debra" is a soulful, tongue-in-cheek R&B-influenced track by Beck, known for its falsetto vocals and humorous, seductive lyrics.
-
E.
Debra Karen
Debra Karen is a film editor best known for her work on the comedy movie "Meatballs."
- 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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a5511a408190973cf5fa1f286a26 |
completed | April 19, 2026, 9:50 a.m. |
Created at: April 10, 2026, 10:20 a.m.