Triple
T9879253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | No, No, No |
E240158
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Mary Brown
Mary Brown is a writer known for her contributions to contemporary literature.
|
E826618
|
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 Brown | Statement: [No, No, No, writer, Mary Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Brown Context triple: [No, No, No, writer, Mary Brown]
-
A.
Mary Bowne
Mary Bowne was a colonial-era New Yorker known primarily as the daughter of Quaker pioneer and religious freedom advocate John Bowne.
-
B.
Mary Pratt
Mary Pratt was a prominent Canadian realist painter renowned for her luminous, intimate depictions of everyday domestic scenes.
-
C.
Mary Wilkes
Mary Wilkes is the daughter of the 18th-century English radical politician and journalist John Wilkes.
-
D.
Margaret Corbin
Margaret Corbin was an American Revolutionary War heroine who took over her fallen husband's cannon during battle, becoming one of the first women to fight in the war and receive a military pension.
-
E.
Susannah Hooker
Susannah Hooker was the wife of prominent Puritan colonial leader and Hartford founder Thomas Hooker, known primarily through her association with his life and ministry.
- 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 Brown Triple: [No, No, No, writer, Mary Brown]
Generated description
Mary Brown is a writer known for her contributions to contemporary literature.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Brown Target entity description: Mary Brown is a writer known for her contributions to contemporary literature.
-
A.
Mary Bowne
Mary Bowne was a colonial-era New Yorker known primarily as the daughter of Quaker pioneer and religious freedom advocate John Bowne.
-
B.
Mary Pratt
Mary Pratt was a prominent Canadian realist painter renowned for her luminous, intimate depictions of everyday domestic scenes.
-
C.
Mary Wilkes
Mary Wilkes is the daughter of the 18th-century English radical politician and journalist John Wilkes.
-
D.
Margaret Corbin
Margaret Corbin was an American Revolutionary War heroine who took over her fallen husband's cannon during battle, becoming one of the first women to fight in the war and receive a military pension.
-
E.
Susannah Hooker
Susannah Hooker was the wife of prominent Puritan colonial leader and Hartford founder Thomas Hooker, known primarily through her association with his life and ministry.
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb4135c108190b3330e929509699d |
completed | April 2, 2026, 12:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e480ead08190992eb43ea3eac38b |
completed | April 5, 2026, 4:26 a.m. |
| NEDg | Description generation | batch_69d1e5d0da7081908e14fe4bc6623ea5 |
completed | April 5, 2026, 4:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1e6af89f88190abe63f8172182f58 |
completed | April 5, 2026, 4:35 a.m. |
Created at: March 30, 2026, 8:37 p.m.