Triple
T8801615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janis Paige |
E209421
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Donna |
E282771
|
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: Donna | Statement: [Janis Paige, givenName, Donna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donna Context triple: [Janis Paige, givenName, Donna]
-
A.
Donna
chosen
Donna is a feminine given name of Italian origin that has been widely used in English-speaking countries.
-
B.
Diane
Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
-
C.
Adrienne
Adrienne is a feminine given name of French origin, commonly used in English- and French-speaking countries.
-
D.
Dyonne
Dyonne is an alternative form of the name Dion, typically used as a given name.
-
E.
Drisella
Drisella is one of Cinderella’s vain and spiteful stepsisters in Disney’s 2015 live-action adaptation of the classic fairy tale.
- 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fb9c5c88190881b069e1face10c |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfa046aabc8190abca2957593edc8b |
completed | April 3, 2026, 11:11 a.m. |
Created at: March 30, 2026, 6:44 p.m.