Triple
T5989948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gina Ruberti |
E133320
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gina |
E307997
|
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: Gina | Statement: [Gina Ruberti, givenName, Gina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gina Context triple: [Gina Ruberti, givenName, Gina]
-
A.
Gina
chosen
Gina is a feminine given name commonly used in English and Italian-speaking countries, often as a short form of names like Regina, Georgina, or Luigina.
-
B.
Gina Shay
Gina Shay is an American film producer best known for her work on animated features at DreamWorks Animation, including the hit musical comedy "Trolls."
-
C.
Gianna
Gianna is a feminine given name of Italian origin, often associated with the late Gianna Bryant, daughter of basketball legend Kobe Bryant.
-
D.
Jenna
Jenna is a common feminine given name, often used as a diminutive or variant of Jennifer.
-
E.
Tina
Tina, formally known as Baroness Stowell of Beeston, is a British Conservative politician and life peer in the House of Lords.
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04dc76fd481908cc3f327e532a1a6 |
completed | March 22, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1135e653481909869094063d31605 |
completed | March 23, 2026, 10:18 a.m. |
Created at: March 22, 2026, 4:05 p.m.