Triple
T4432899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlene |
E95375
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Charleen |
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: Charleen | Statement: [Charlene, hasVariant, Charleen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charleen Context triple: [Charlene, hasVariant, Charleen]
-
A.
Charlene
chosen
Charlene is a feminine given name derived from the male name Charles.
-
B.
Colleen
Colleen is a feminine given name of Irish origin, commonly used in English-speaking countries.
-
C.
Glennis
Glennis is a feminine given name, best known for belonging to Glennis Dickhouse Yeager, the wife of test pilot Chuck Yeager and namesake of the Bell X-1 aircraft "Glamorous Glennis."
-
D.
Carole
Carole is a feminine given name of French origin, commonly used in English-speaking countries.
-
E.
Celia Mae
Celia Mae is the one-eyed, snake-haired receptionist at Monsters, Inc. and Mike Wazowski’s girlfriend in the Pixar animated film.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3556cd83881908547aa311c4f17fa |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6137171148190b77a6f783d5cf315 |
completed | March 15, 2026, 2:03 a.m. |
Created at: March 12, 2026, 11:31 p.m.