Triple
T19572049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Road Trip |
E489740
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Beth Wagner
Beth Wagner is the central protagonist of the comedy film "Road Trip," around whom the misadventures and plot primarily revolve.
|
E1385038
|
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: Beth Wagner | Statement: [Road Trip, mainCharacter, Beth Wagner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beth Wagner Context triple: [Road Trip, mainCharacter, Beth Wagner]
-
A.
Jane Wagner
Jane Wagner is an American writer, director, and producer best known for her long-running creative collaboration and personal partnership with comedian and actress Lily Tomlin.
-
B.
Eva Wagner
Eva Wagner was a daughter of the famed German composer Richard Wagner, belonging to the prominent Wagner family closely associated with the Bayreuth Festival.
-
C.
Miriam Wagner
Miriam Wagner was the wife of American television host and comedian Jack Paar.
-
D.
Anna Werner
Anna Werner was the wife of prominent Polish industrialist Karol Scheibler, associated with the influential Scheibler family in Łódź.
-
E.
Eva Kroll
Eva Kroll is a film editor best known for her work on the classic anti-war movie "Paths of Glory."
- 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: Beth Wagner Triple: [Road Trip, mainCharacter, Beth Wagner]
Generated description
Beth Wagner is the central protagonist of the comedy film "Road Trip," around whom the misadventures and plot primarily revolve.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Beth Wagner Target entity description: Beth Wagner is the central protagonist of the comedy film "Road Trip," around whom the misadventures and plot primarily revolve.
-
A.
Jane Wagner
Jane Wagner is an American writer, director, and producer best known for her long-running creative collaboration and personal partnership with comedian and actress Lily Tomlin.
-
B.
Eva Wagner
Eva Wagner was a daughter of the famed German composer Richard Wagner, belonging to the prominent Wagner family closely associated with the Bayreuth Festival.
-
C.
Miriam Wagner
Miriam Wagner was the wife of American television host and comedian Jack Paar.
-
D.
Anna Werner
Anna Werner was the wife of prominent Polish industrialist Karol Scheibler, associated with the influential Scheibler family in Łódź.
-
E.
Eva Kroll
Eva Kroll is a film editor best known for her work on the classic anti-war movie "Paths of Glory."
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6402103208190b80acdfa82b7a9c4 |
completed | April 20, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a075f0b1ce08190bdaf79f017fea675 |
completed | May 15, 2026, 5:59 p.m. |
| NEDg | Description generation | batch_6a075f8c106481908e6fd7631ae69842 |
completed | May 15, 2026, 6:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a076070784c81908d28306344363c53 |
completed | May 15, 2026, 6:05 p.m. |
Created at: April 10, 2026, 1:42 p.m.