Triple
T16138442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessica |
E391589
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object | Jessi |
E890112
|
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: Jessi | Statement: [Jessica, hasNickname, Jessi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessi Context triple: [Jessica, hasNickname, Jessi]
-
A.
Jessi
chosen
Jessi is the 1976 country music album by American singer Jessi Colter, noted for its outlaw country style and emotional, introspective songs.
-
B.
Jessie
Jessie is the given name of Jessie James Combs, an American television personality and professional racer known for her work on automotive and metal fabrication shows.
-
C.
Jessie
Jessie is a spirited, yodeling cowgirl doll from the Toy Story franchise known for her energetic personality and emotional backstory.
-
D.
Jessie
Jessie is a given name associated with the acclaimed British-American actress Jessica Tandy, known for her distinguished stage and film career.
-
E.
Jessie
Jessie is a Disney Channel comedy television series starring Debby Ryan as a young nanny working for a wealthy New York City family.
- 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_69d87f1bb0988190b490d273dbf3fd03 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21a06e0988190b5cd62d422d058a2 |
completed | April 17, 2026, 11:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fffef0f51c8190bc039150af8ebf98 |
completed | May 10, 2026, 3:43 a.m. |
Created at: April 10, 2026, 5:01 a.m.