Triple
T8572521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamara Tunie |
E202961
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tamara |
E250443
|
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: Tamara | Statement: [Tamara Tunie, givenName, Tamara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamara Context triple: [Tamara Tunie, givenName, Tamara]
-
A.
Tamara
chosen
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
B.
Alessandra
Alessandra is an Italian politician, former actress, and granddaughter of Benito Mussolini.
-
C.
Tania
Tania is a feminine given name commonly used as a diminutive or variant of names like Tatyana or Tatiana.
-
D.
Melina
Melina is a key resistance fighter and love interest in the science fiction film "Total Recall," known for aiding the protagonist in his struggle against a corrupt Martian regime.
-
E.
Nina
Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea43843c8190ac2224d427bb7a75 |
completed | March 31, 2026, 3:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef3265be481909acfef718e2bd403 |
completed | April 2, 2026, 10:52 p.m. |
Created at: March 30, 2026, 6:21 p.m.