Triple
T1822877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessica Barth |
E40579
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Jessica
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
|
E252429
|
NE FINISHED |
How this triple was built (4 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 Barth, givenName, Jessica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Context triple: [Jessica Barth, givenName, Jessica]
-
A.
Emily
Emily Warren Roebling was a pioneering 19th-century American engineer best known for her crucial role in overseeing the completion of the Brooklyn Bridge.
-
B.
Sarah
Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
-
C.
Jennifer
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
-
D.
Jane
Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
-
E.
Emma
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jessica Triple: [Jessica Barth, givenName, Jessica]
Generated description
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jessica Target entity description: Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
-
A.
Emily
Emily Warren Roebling was a pioneering 19th-century American engineer best known for her crucial role in overseeing the completion of the Brooklyn Bridge.
-
B.
Sarah
Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
-
C.
Jennifer
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
-
D.
Jane
Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
-
E.
Emma
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
- F. None of above. chosen
Provenance (5 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_69a8864526c081908a3a4d74f689e2c5 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa662d13e88190a22b0faf0d848c7d |
completed | March 6, 2026, 5:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7edf084881908bb8db8e0348bfc8 |
completed | March 9, 2026, 8:03 a.m. |
| NEDg | Description generation | batch_69ae7faa64e48190b82e48165a931598 |
completed | March 9, 2026, 8:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae801a87a88190ae461ca164b358a4 |
completed | March 9, 2026, 8:08 a.m. |
Created at: March 4, 2026, 7:32 p.m.