Triple
T14593397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derry Girls |
E342500
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object |
Liz Lewin
Liz Lewin is a television producer best known for her executive production work on the acclaimed comedy series "Derry Girls."
|
E1131293
|
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: Liz Lewin | Statement: [Derry Girls, executiveProducer, Liz Lewin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liz Lewin Context triple: [Derry Girls, executiveProducer, Liz Lewin]
-
A.
Renee Luskin
Renee Luskin is a philanthropist and benefactor whose support and contributions led to the naming of UCLA’s Luskin School of Public Affairs in her honor.
-
B.
Liz Gorinsky
Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
-
C.
Liza Snyder
Liza Snyder is an American television actress best known for her comedic roles on sitcoms such as "Yes, Dear" and "Man with a Plan."
-
D.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
E.
Jodi Wexler
Jodi Wexler is an actress best known for her role in the film "The Love Machine."
- 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: Liz Lewin Triple: [Derry Girls, executiveProducer, Liz Lewin]
Generated description
Liz Lewin is a television producer best known for her executive production work on the acclaimed comedy series "Derry Girls."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Liz Lewin Target entity description: Liz Lewin is a television producer best known for her executive production work on the acclaimed comedy series "Derry Girls."
-
A.
Renee Luskin
Renee Luskin is a philanthropist and benefactor whose support and contributions led to the naming of UCLA’s Luskin School of Public Affairs in her honor.
-
B.
Liz Gorinsky
Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
-
C.
Liza Snyder
Liza Snyder is an American television actress best known for her comedic roles on sitcoms such as "Yes, Dear" and "Man with a Plan."
-
D.
Rachel Leibowitz
Rachel Leibowitz is a person notable enough to be specifically cited as a bearer of the surname Leibowitz.
-
E.
Jodi Wexler
Jodi Wexler is an actress best known for her role in the film "The Love Machine."
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43480d8819084a707e56da2c237 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe968124d481909dd89dd9282788b7 |
completed | May 9, 2026, 2:05 a.m. |
| NEDg | Description generation | batch_69fe97c0a3688190a9f55376a1d7ad87 |
completed | May 9, 2026, 2:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe985cec608190888733cdc5bd71ea |
completed | May 9, 2026, 2:13 a.m. |
Created at: April 10, 2026, 1:24 a.m.