Triple
T18550895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessica Nolan |
E453374
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jessica |
—
|
NE NERFINISHED |
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: Jessica | Statement: [Jessica Nolan, givenName, Jessica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Context triple: [Jessica Nolan, givenName, Jessica]
-
A.
Jessica
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
-
B.
Jessica
Jessica is a kind-hearted schoolteacher who becomes Mrs. Claus in the classic stop-motion Christmas special "Santa Claus Is Comin' to Town."
-
C.
Jessica
chosen
Jessica is a feminine given name of Hebrew origin, widely used in English-speaking countries and popularized by Shakespeare’s play "The Merchant of Venice."
-
D.
Jessica
Jessica is a character from the science fiction novel "Dirty Hands," likely involved in its morally complex, politically charged narrative.
-
E.
Jessica
Jessica is the main female character in the action film "Kiss of the Dragon," where she becomes entangled in the protagonist's dangerous mission.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d388b0c881908e610a1c45b52640 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53800c0fc819097782f1e81574598 |
completed | April 19, 2026, 8:16 p.m. |
Created at: April 10, 2026, 11:38 a.m.