Triple
T11170966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melissa |
E264271
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Mellisa |
E264271
|
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: Mellisa | Statement: [Melissa, hasVariant, Mellisa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mellisa Context triple: [Melissa, hasVariant, Mellisa]
-
A.
Melissa
Melissa is a small but rapidly growing suburban city in North Texas, located within the Dallas–Fort Worth metropolitan area.
-
B.
Melissa
"Melissa" is a classic, melodic Southern rock ballad by the Allman Brothers Band, known for its gentle acoustic sound and reflective lyrics.
-
C.
Melissa
chosen
Melissa is a feminine given name commonly used in English-speaking countries, derived from the Greek word for "honeybee."
-
D.
Melinda
Melinda is a young, impressionable girl in the play "Inherit the Wind," serving as a minor character who reflects the town’s attitudes during the famous trial.
-
E.
Melinda
Melinda is a central female character in George Farquhar’s Restoration comedy "The Recruiting Officer," known for her wit, independence, and role in the play’s romantic intrigues.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e89660208190b1d9e91529f5d246 |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e483816af08190877f86ee52846581 |
completed | April 19, 2026, 7:25 a.m. |
Created at: April 8, 2026, 9:29 p.m.