Triple
T13240405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GLOW |
E315264
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object |
Tara Herrmann
Tara Herrmann is a television producer best known for her executive production work on the Netflix series "GLOW."
|
E1041995
|
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: Tara Herrmann | Statement: [GLOW, executiveProducer, Tara Herrmann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tara Herrmann Context triple: [GLOW, executiveProducer, Tara Herrmann]
-
A.
Erika Tymrak
Erika Tymrak is an American professional soccer midfielder known for her playmaking creativity in the National Women's Soccer League and appearances with the United States women's national team.
-
B.
Tara Martin
Tara Martin is a fictional character from the soap opera "All My Children," known as one of the early members of the Martin family.
-
C.
Kirsten Fudeman
Kirsten Fudeman is a linguist and scholar known for her collaborative work with Mark Aronoff in the field of morphology and the history of linguistic thought.
-
D.
Stefanie Ehrlich
Stefanie Ehrlich is known as a child of the prominent American biologist and author Paul Ehrlich.
-
E.
Tessa Berens
Tessa Berens is a fictional character from the work titled "The Silence."
- 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: Tara Herrmann Triple: [GLOW, executiveProducer, Tara Herrmann]
Generated description
Tara Herrmann is a television producer best known for her executive production work on the Netflix series "GLOW."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tara Herrmann Target entity description: Tara Herrmann is a television producer best known for her executive production work on the Netflix series "GLOW."
-
A.
Erika Tymrak
Erika Tymrak is an American professional soccer midfielder known for her playmaking creativity in the National Women's Soccer League and appearances with the United States women's national team.
-
B.
Tara Martin
Tara Martin is a fictional character from the soap opera "All My Children," known as one of the early members of the Martin family.
-
C.
Kirsten Fudeman
Kirsten Fudeman is a linguist and scholar known for her collaborative work with Mark Aronoff in the field of morphology and the history of linguistic thought.
-
D.
Stefanie Ehrlich
Stefanie Ehrlich is known as a child of the prominent American biologist and author Paul Ehrlich.
-
E.
Tessa Berens
Tessa Berens is a fictional character from the work titled "The Silence."
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d5850ac8190849a51da39efe5be |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7460e94a08190a518f466f55db482 |
completed | May 3, 2026, 12:56 p.m. |
| NEDg | Description generation | batch_69f74b9dac6c8190b1fc3ed04fcf6d1f |
completed | May 3, 2026, 1:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f74c5195188190bad111b301713426 |
completed | May 3, 2026, 1:23 p.m. |
Created at: April 9, 2026, 9:23 p.m.