Triple
T3907440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Allerton |
E87235
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Mary
Mary Allerton was a Mayflower passenger and one of the early settlers of Plymouth Colony in 17th-century New England.
|
E398976
|
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: [Mary Allerton, givenName, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Mary Allerton, givenName, Mary]
-
A.
Mary
Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
-
B.
Mary
Mary is the given first name of Margaret Truman, the daughter of U.S. President Harry S. Truman and a noted author and singer.
-
C.
Mary
Mary is the given first name of the acclaimed American actress Meryl Streep.
-
D.
Mary
Mary is a minor character in Mark Twain's novel "The Adventures of Tom Sawyer," known as Tom's kind and well-behaved cousin.
-
E.
Mary
Mary Eleanor Darwin was a member of the Darwin family, known primarily as a descendant of the naturalist Charles Darwin.
- 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: [Mary Allerton, givenName, Mary]
Generated description
Mary Allerton was a Mayflower passenger and one of the early settlers of Plymouth Colony in 17th-century New England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Target entity description: Mary Allerton was a Mayflower passenger and one of the early settlers of Plymouth Colony in 17th-century New England.
-
A.
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.
-
B.
Mary
Mary is the birth name of May Morris, the English artisan, designer, and key figure in the Arts and Crafts movement.
-
C.
Mary
Mary is the given name of Mary Wollstonecraft, the pioneering 18th-century English writer and advocate of women's rights.
-
D.
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.
-
E.
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.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeed1290e48190aaf2d8b2a7be707a |
completed | March 9, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b52845684c8190b6f0676319a6fc3c |
completed | March 14, 2026, 9:20 a.m. |
| NEDg | Description generation | batch_69b5290462a88190892c0bcc3a74f2fa |
completed | March 14, 2026, 9:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b529600884819098cb208e38e6281a |
completed | March 14, 2026, 9:24 a.m. |
Created at: March 9, 2026, 3:22 p.m.