Triple
T3134552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lynn Swann |
E65495
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Charena Swann
Charena Swann is the wife of former NFL star and Pro Football Hall of Famer Lynn Swann.
|
E330626
|
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: Charena Swann | Statement: [Lynn Swann, spouse, Charena Swann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charena Swann Context triple: [Lynn Swann, spouse, Charena Swann]
-
A.
Princess Leia
Princess Leia is a courageous Rebel leader and princess who becomes one of the central heroes of the original Star Wars trilogy.
-
B.
Cara Dune
Cara Dune is a former Rebel shock trooper turned mercenary who becomes a key ally to the titular bounty hunter in the Star Wars series "The Mandalorian."
-
C.
Padmé Amidala
Padmé Amidala is a courageous and idealistic queen-turned-senator from the Star Wars saga, known for her political leadership, diplomacy, and pivotal role in the fall of the Republic and rise of the Empire.
-
D.
Kate Mara
Kate Mara is an American actress known for her roles in films like "The Martian" and "Brokeback Mountain" and TV series such as "House of Cards."
-
E.
Marella Ciano
Marella Ciano was an Italian aristocrat and socialite, best known as the daughter of Edda Mussolini and granddaughter of dictator Benito Mussolini.
- 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: Charena Swann Triple: [Lynn Swann, spouse, Charena Swann]
Generated description
Charena Swann is the wife of former NFL star and Pro Football Hall of Famer Lynn Swann.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Charena Swann Target entity description: Charena Swann is the wife of former NFL star and Pro Football Hall of Famer Lynn Swann.
-
A.
Princess Leia
Princess Leia is a courageous Rebel leader and princess who becomes one of the central heroes of the original Star Wars trilogy.
-
B.
Cara Dune
Cara Dune is a former Rebel shock trooper turned mercenary who becomes a key ally to the titular bounty hunter in the Star Wars series "The Mandalorian."
-
C.
Padmé Amidala
Padmé Amidala is a courageous and idealistic queen-turned-senator from the Star Wars saga, known for her political leadership, diplomacy, and pivotal role in the fall of the Republic and rise of the Empire.
-
D.
Kate Mara
Kate Mara is an American actress known for her roles in films like "The Martian" and "Brokeback Mountain" and TV series such as "House of Cards."
-
E.
Marella Ciano
Marella Ciano was an Italian aristocrat and socialite, best known as the daughter of Edda Mussolini and granddaughter of dictator Benito Mussolini.
- 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_69ad8581c25c8190b0d85ba9b9baa531 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada562540081908627950dd0b56a1e |
completed | March 8, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f84d8288190b1f48fa0f5c10773 |
completed | March 12, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69b2102e35b08190ad9ca397f0c937da |
completed | March 12, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b21458b07081909d75886e0d9f88e9 |
completed | March 12, 2026, 1:18 a.m. |
Created at: March 8, 2026, 3:05 p.m.