Triple
T4043759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caitlyn Jenner |
E84012
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object |
Caitlyn
Caitlyn is the given name of Caitlyn Jenner, the American television personality and former Olympic gold medal–winning decathlete.
|
E408615
|
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: Caitlyn | Statement: [Caitlyn Jenner, hasGivenName, Caitlyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caitlyn Context triple: [Caitlyn Jenner, hasGivenName, Caitlyn]
-
A.
Katarina
Katarina is a feminine given name, commonly used in various European cultures, that is a variant of the name Catherine.
-
B.
Leona
Leona is a feminine given name used in various cultures, often derived from the Latin word for "lion."
-
C.
Ashe
Ashe is a small village in Hampshire, England, known for its rural setting near the headwaters of the River Test.
-
D.
D.Va
D.Va is a popular hero from the game Overwatch, known as a former pro gamer who pilots a high-tech mech in fast-paced combat.
-
E.
Tristana
Tristana is a 1970 Spanish drama film directed by Luis Buñuel, known for its exploration of power, morality, and desire through the story of a young woman and her older guardian.
- 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: Caitlyn Triple: [Caitlyn Jenner, hasGivenName, Caitlyn]
Generated description
Caitlyn is the given name of Caitlyn Jenner, the American television personality and former Olympic gold medal–winning decathlete.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Caitlyn Target entity description: Caitlyn is the given name of Caitlyn Jenner, the American television personality and former Olympic gold medal–winning decathlete.
-
A.
Katarina
Katarina is a feminine given name, commonly used in various European cultures, that is a variant of the name Catherine.
-
B.
Leona
Leona is a feminine given name used in various cultures, often derived from the Latin word for "lion."
-
C.
Ashe
Ashe is a small village in Hampshire, England, known for its rural setting near the headwaters of the River Test.
-
D.
D.Va
D.Va is a popular hero from the game Overwatch, known as a former pro gamer who pilots a high-tech mech in fast-paced combat.
-
E.
Tristana
Tristana is a 1970 Spanish drama film directed by Luis Buñuel, known for its exploration of power, morality, and desire through the story of a young woman and her older guardian.
- 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_69aed930bd5c819083e7dcc14fc44f69 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb5d759c8190b61fbbe94ffe2bf7 |
completed | March 9, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5564fb54c81909f40ca1d6f1e521e |
completed | March 14, 2026, 12:36 p.m. |
| NEDg | Description generation | batch_69b5579085608190937528de7e0f987e |
completed | March 14, 2026, 12:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b55828506081908181436282907b08 |
completed | March 14, 2026, 12:44 p.m. |
Created at: March 9, 2026, 3:37 p.m.