Triple
T14397228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessica Tandy |
E356980
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jessie |
E356981
|
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: Jessie | Statement: [Jessica Tandy, givenName, Jessie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessie Context triple: [Jessica Tandy, givenName, Jessie]
-
A.
Jessie
Jessie is a given name commonly used as a diminutive or variant of names like Jessica or Jesse.
-
B.
Jessie
Jessie is the central character in the 2006 British comedy-drama film "Venus," around whom the story’s emotional and relational tensions revolve.
-
C.
Jessie
chosen
Jessie is a given name associated with the acclaimed British-American actress Jessica Tandy, known for her distinguished stage and film career.
-
D.
Jessie
Jessie is a person whose full name is Jessie Oriana Huxley.
-
E.
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.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90826f908190b3969af9b7cf922f |
completed | April 14, 2026, 7:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd551cbdb08190a9ea53e607f2555b |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:17 a.m.