Triple
T8645438
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tami-Lynn |
E204761
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Jessica Barth |
E40579
|
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: Jessica Barth | Statement: [Tami-Lynn, portrayedBy, Jessica Barth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Barth Context triple: [Tami-Lynn, portrayedBy, Jessica Barth]
-
A.
Jessica Barth
chosen
Jessica Barth is an American actress best known for playing Tami-Lynn in the comedy films "Ted" and "Ted 2."
-
B.
Kate Barry
Kate Barry was a British-French photographer known for her intimate portraits of artists and celebrities, and as the daughter of Jane Birkin and composer John Barry.
-
C.
Darcie Schollmeyer
Darcie Schollmeyer is known as the wife of Hockey Hall of Famer and former NHL star Brett Hull.
-
D.
Ryan Kiera Armstrong
Ryan Kiera Armstrong is an American child actress known for her roles in films such as "The Tomorrow War" and various television series.
-
E.
Kate Burroughs
Kate Burroughs is the central protagonist of the film "The Four Seasons," around whom the story’s relationships and events revolve.
- 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_69ca834ca1c88190a11ffb0200342fac |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc479999c881908c0c4e01c07d02d4 |
completed | March 31, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef35ea7108190b164f9c3715be4f5 |
completed | April 2, 2026, 10:53 p.m. |
Created at: March 30, 2026, 6:28 p.m.