Triple
T9550765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte |
E230414
|
entity |
| Predicate | hasRelatedName |
P3889
|
FINISHED |
| Object | Charlene |
E95375
|
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: Charlene | Statement: [Charlotte, hasRelatedName, Charlene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charlene Context triple: [Charlotte, hasRelatedName, Charlene]
-
A.
Charlene
chosen
Charlene is a feminine given name derived from the male name Charles.
-
B.
Cherie
Cherie is the naive yet determined young woman who becomes the romantic focus of the cowboy in the classic stage play and film "Bus Stop."
-
C.
Darlene
Darlene is a fictional character portrayed by actress Dominique Fishback, known from her work in film and television dramas.
-
D.
Carole
Carole is a feminine given name of French origin, commonly used in English-speaking countries.
-
E.
Charlene Fusco
Charlene Fusco is best known as the wife of American comedian and actor Tim Conway.
- 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_69ca847d3be8819099c9dad2a7e786f1 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd991df7308190a56d95f195627513 |
completed | April 1, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1528068a481908d5c8037b4591db5 |
completed | April 4, 2026, 6:03 p.m. |
Created at: March 30, 2026, 8:02 p.m.