Triple
T15748053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Fuller |
E381768
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Mary
Mary was the wife of English churchman and historian Thomas Fuller, known for her connection to this notable 17th-century writer and clergyman.
|
E1174316
|
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: [Thomas Fuller, spouse, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Thomas Fuller, spouse, Mary]
-
A.
Mary
Mary is the middle name of Edith Tolkien, the wife of author J.R.R. Tolkien.
-
B.
Mary
Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
-
C.
Mary
Mary is the given name of the American stage and film actress Josephine Hull, known for her roles in classic mid-20th-century theater and cinema.
-
D.
Mary
Mary is the given first name of the American actress Elinor Donahue, known for her roles in classic television series.
-
E.
Mary
Mary is the birth name of American actress Sean Young, known for her roles in films such as "Blade Runner" and "Dune."
- 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: [Thomas Fuller, spouse, Mary]
Generated description
Mary was the wife of English churchman and historian Thomas Fuller, known for her connection to this notable 17th-century writer and clergyman.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Target entity description: Mary was the wife of English churchman and historian Thomas Fuller, known for her connection to this notable 17th-century writer and clergyman.
-
A.
Mary
Mary, Princess Royal and Princess of Orange, was the eldest daughter of King Charles I of England and the wife of William II of Orange, making her a key figure in 17th-century Anglo-Dutch royal relations.
-
B.
Mary
Mary is the given name of Lady Mary Wortley Montagu, an 18th-century English aristocrat, writer, and early advocate of smallpox inoculation.
-
C.
Mary
Mary II of England was a late 17th-century Queen of England, Scotland, and Ireland who ruled jointly with her husband William III after the Glorious Revolution.
-
D.
Mary
Mary is the given name of Mary Sidney, an English Renaissance noblewoman, writer, and literary patron.
-
E.
Mary
Mary is the given name of Lady Mary Coke, an 18th-century British noblewoman and diarist known for her detailed letters and journals.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0502d72008190b4d13a6b3a12e467 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff8309cba881909579ee5a62b3aa31 |
completed | May 9, 2026, 6:55 p.m. |
| NEDg | Description generation | batch_69ff83d929a48190aea75597b864d210 |
completed | May 9, 2026, 6:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff846436e48190b711da134c9a3b81 |
completed | May 9, 2026, 7 p.m. |
Created at: April 10, 2026, 4:46 a.m.