Triple
T8381908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marg Helgenberger |
E197711
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Mary
Mary is the given first name of American actress Marg Helgenberger, known for her role on the television series "CSI: Crime Scene Investigation."
|
E731767
|
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: Mary | Statement: [Marg Helgenberger, givenName, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Marg Helgenberger, givenName, Mary]
-
A.
Mary
Mary is the given name of Mary Wollstonecraft, the pioneering 18th-century English writer and advocate of women's rights.
-
B.
Mary
Mary is the middle name of Theresa May, the former Prime Minister of the United Kingdom.
-
C.
Mary
Mary I of England was the 16th-century Queen of England and Ireland best known for her attempt to restore Roman Catholicism and for the Marian persecutions that earned her the nickname "Bloody Mary."
-
D.
Mary
Mary is a studio album by Ghanaian rapper Sarkodie, known for its highlife influences and tribute to his late grandmother.
-
E.
Mary
Mary Allerton was a Mayflower passenger and one of the early settlers of Plymouth Colony in 17th-century New England.
- 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: Mary Triple: [Marg Helgenberger, givenName, Mary]
Generated description
Mary is the given first name of American actress Marg Helgenberger, known for her role on the television series "CSI: Crime Scene Investigation."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Target entity description: Mary is the given first name of American actress Marg Helgenberger, known for her role on the television series "CSI: Crime Scene Investigation."
-
A.
Mary
Mary is the given name of American character actress Marjorie Main, known for her roles in classic Hollywood films.
-
B.
Mary
Mary is the given first name of the acclaimed American actress Meryl Streep.
-
C.
Mary
Mary is the given name of the American suspense novelist Mary Higgins Clark, known for her bestselling mystery and thriller books.
-
D.
Mary
Mary is the given name of American actress and television icon Mary Tyler Moore, best known for her roles in "The Dick Van Dyke Show" and "The Mary Tyler Moore Show."
-
E.
Mary
Mary is the given name of American silent film actress Mae Marsh, known for her roles in early 20th-century cinema.
- 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_69ca82f64c188190af4e1608036b865d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80dc96048190887d7df8bce5c1fd |
completed | March 31, 2026, 8:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce02c0664481908f3c79246350a248 |
completed | April 2, 2026, 5:46 a.m. |
| NEDg | Description generation | batch_69ce064211e48190b558d4355be659ba |
completed | April 2, 2026, 6:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce07a390048190ac26a7e3d3d561e0 |
completed | April 2, 2026, 6:07 a.m. |
Created at: March 30, 2026, 6:02 p.m.