Triple
T6311582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abby Wambach |
E141515
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Mary
Mary is the first name of American soccer legend Abby Wambach, one of the most prolific goal scorers in international women’s football.
|
E585875
|
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: [Abby Wambach, givenName, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Abby Wambach, 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: [Abby Wambach, givenName, Mary]
Generated description
Mary is the first name of American soccer legend Abby Wambach, one of the most prolific goal scorers in international women’s football.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Target entity description: Mary is the first name of American soccer legend Abby Wambach, one of the most prolific goal scorers in international women’s football.
-
A.
Mary
Mary is the given name of Mary J. Blige, the acclaimed American singer, songwriter, and actress often called the "Queen of Hip-Hop Soul."
-
B.
Mary
Mary is the given first name of the acclaimed American actress Meryl Streep.
-
C.
Mary
Mary is the given name of Mary Church Terrell, a prominent African American civil rights activist, educator, and suffragist in the late 19th and early 20th centuries.
-
D.
Mary
Mary is the given name of Mary Wollstonecraft, the pioneering 18th-century English writer and advocate of women's rights.
-
E.
Mary
Mary is the birth name of American actress, singer, and dancer Debbie Reynolds, a major Hollywood star of the mid-20th century.
- 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_69c008d00efc8190a36c05b4b4a3bf4b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0649d1e048190a3fc7fbce9d2ee57 |
completed | March 22, 2026, 9:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6040337908190a10f7cd9c1264267 |
completed | March 27, 2026, 4:13 a.m. |
| NEDg | Description generation | batch_69c6056435b481908a63a880b7bcd489 |
completed | March 27, 2026, 4:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c605f2369c819080fd52282b20437e |
completed | March 27, 2026, 4:22 a.m. |
Created at: March 22, 2026, 4:28 p.m.