Triple
T21211169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeffrey Reddick |
E522722
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | Tamara |
—
|
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: Tamara | Statement: [Jeffrey Reddick, wrote, Tamara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamara Context triple: [Jeffrey Reddick, wrote, Tamara]
-
A.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
B.
Tamara
chosen
Tamara is a 2005 supernatural horror film written by Jeffrey Reddick about a bullied high school girl who returns from the dead to exact revenge.
-
C.
Alessandra
Alessandra is an Italian politician, former actress, and granddaughter of Benito Mussolini.
-
D.
Alessandra
Alessandra is an Italian given name, the feminine form of Alessandro, equivalent to Alexandra in English.
-
E.
Tamara Muro
Tamara Muro is a business associate of Christine Baumgartner, known in connection with Baumgartner’s professional ventures.
- 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_69e0b511ed84819099b449b4a111085c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7346eb20c8190aeb3c0cc0a24aaf9 |
completed | April 21, 2026, 8:25 a.m. |
Created at: April 16, 2026, 3:37 p.m.