Triple
T3675896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donna Tartt |
E77990
|
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: [Donna Tartt, givenName, Donna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Donna Context triple: [Donna Tartt, 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.
Drisella
Drisella is one of Cinderella’s vain and spiteful stepsisters in Disney’s 2015 live-action adaptation of the classic fairy tale.
-
E.
Sandra
Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
- 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_69ad85e18c1c8190be8aafb227f39f48 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc462ffdc8190896e9f98f648e2f3 |
completed | March 8, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b48856b7d481909d9cc32586d61d44 |
completed | March 13, 2026, 9:57 p.m. |
Created at: March 8, 2026, 3:25 p.m.