Triple
T2316970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houston Cougars |
E51086
|
entity |
| Predicate | mascot |
P52
|
FINISHED |
| Object |
Sasha
Sasha is one of the costumed cougar mascots representing the University of Houston's athletic teams, the Houston Cougars.
|
E256466
|
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: Sasha | Statement: [Houston Cougars, mascot, Sasha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sasha Context triple: [Houston Cougars, mascot, Sasha]
-
A.
Sasha
Sasha is a common Russian diminutive form of the given name Alexander (and also Alexandra).
-
B.
Sonya
Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
-
C.
Tanya
Tanya is the foundational Chabad-Lubavitch Hasidic work by Rabbi Shneur Zalman of Liadi, presenting a systematic approach to Jewish mysticism, psychology, and spiritual self-improvement.
-
D.
Sasha Alexander
Sasha Alexander is an American actress best known for her television roles on series such as NCIS and Rizzoli & Isles.
-
E.
Anya
Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
- 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: Sasha Triple: [Houston Cougars, mascot, Sasha]
Generated description
Sasha is one of the costumed cougar mascots representing the University of Houston's athletic teams, the Houston Cougars.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sasha Target entity description: Sasha is one of the costumed cougar mascots representing the University of Houston's athletic teams, the Houston Cougars.
-
A.
Sasha
Sasha is a common Russian diminutive form of the given name Alexander (and also Alexandra).
-
B.
Sonya
Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
-
C.
Tanya
Tanya is the foundational Chabad-Lubavitch Hasidic work by Rabbi Shneur Zalman of Liadi, presenting a systematic approach to Jewish mysticism, psychology, and spiritual self-improvement.
-
D.
Sasha Alexander
Sasha Alexander is an American actress best known for her television roles on series such as NCIS and Rizzoli & Isles.
-
E.
Anya
Anya is a person known primarily through her relationship to someone named Hannah, likely as a friend or family member.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc62df2048190ac7a5ebc0a4139b2 |
completed | March 7, 2026, 6:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae8964902081909070dd03ccb7cf1f |
completed | March 9, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_69ae8ab5bf78819085120418a26cbe28 |
completed | March 9, 2026, 8:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae8b2a89788190975ab66f432f834f |
completed | March 9, 2026, 8:56 a.m. |
Created at: March 4, 2026, 7:49 p.m.