Triple
T15811082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hang Time |
E383353
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Daniella Deutscher
Daniella Deutscher is an American actress best known for playing Julie Connor on the 1990s teen sports sitcom "Hang Time."
|
E1194130
|
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: Daniella Deutscher | Statement: [Hang Time, hasCastMember, Daniella Deutscher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniella Deutscher Context triple: [Hang Time, hasCastMember, Daniella Deutscher]
-
A.
Daphne Kluger
Daphne Kluger is a glamorous, high-profile actress and the unsuspecting target of the jewel heist in the film "Ocean's 8."
-
B.
Claudia Finkelstein
Claudia Finkelstein is a physician and academic known for her work in internal medicine and physician well-being.
-
C.
Alisa Freindlich
Alisa Freindlich is a renowned Soviet and Russian actress celebrated for her work in film and theater, particularly in the late 20th century.
-
D.
Tatia Rosenthal
Tatia Rosenthal is an Israeli-born film director and animator best known for her stop-motion feature "9.99$" and her work in independent animation.
-
E.
Tamira Goldstein
Tamira Goldstein is a main character on the teen comedy series "Breaker High," known for her sharp wit and strong personality among the shipboard high school students.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Daniella Deutscher Triple: [Hang Time, hasCastMember, Daniella Deutscher]
Generated description
Daniella Deutscher is an American actress best known for playing Julie Connor on the 1990s teen sports sitcom "Hang Time."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniella Deutscher Target entity description: Daniella Deutscher is an American actress best known for playing Julie Connor on the 1990s teen sports sitcom "Hang Time."
-
A.
Daphne Kluger
Daphne Kluger is a glamorous, high-profile actress and the unsuspecting target of the jewel heist in the film "Ocean's 8."
-
B.
Claudia Finkelstein
Claudia Finkelstein is a physician and academic known for her work in internal medicine and physician well-being.
-
C.
Alisa Freindlich
Alisa Freindlich is a renowned Soviet and Russian actress celebrated for her work in film and theater, particularly in the late 20th century.
-
D.
Tatia Rosenthal
Tatia Rosenthal is an Israeli-born film director and animator best known for her stop-motion feature "9.99$" and her work in independent animation.
-
E.
Tamira Goldstein
Tamira Goldstein is a main character on the teen comedy series "Breaker High," known for her sharp wit and strong personality among the shipboard high school students.
- 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_69d86da2858c819090cc8481e7207b6e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b52aae14819091de08630e7e1d1a |
completed | April 16, 2026, 10:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeb7f3c9481908bdde67998263c5e |
completed | May 10, 2026, 2:20 a.m. |
| NEDg | Description generation | batch_69ffecb8c71481908b4913bb078b6415 |
completed | May 10, 2026, 2:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffed469a5c8190932fa4ebc44358c4 |
completed | May 10, 2026, 2:28 a.m. |
Created at: April 10, 2026, 4:49 a.m.