Triple
T6524958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woodie Flowers |
E151278
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Margaret Flowers
Margaret Flowers is known as the wife of the late MIT professor and robotics education pioneer Woodie Flowers.
|
E692413
|
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: Margaret Flowers | Statement: [Woodie Flowers, spouse, Margaret Flowers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margaret Flowers Context triple: [Woodie Flowers, spouse, Margaret Flowers]
-
A.
Margaret Gibson
Margaret Gibson was the wife of American actor Noah Beery, associated with the early Hollywood film era.
-
B.
Margaret Tucker
Margaret Tucker was an Aboriginal Australian activist and one of the country’s first female Indigenous authors, known for her pioneering work in civil rights and welfare for Aboriginal people.
-
C.
Margaret Whitmore
Margaret Whitmore is best known as the wife of American physician-turned-thriller novelist Robin Cook.
-
D.
Margaret Ann Dixon
Margaret Ann Dixon is best known as the wife of acclaimed Canadian film director and producer Norman Jewison.
-
E.
Marjorie Fowler
Marjorie Fowler was an American film editor known for her work on numerous Hollywood productions from the 1950s through the 1970s.
- 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: Margaret Flowers Triple: [Woodie Flowers, spouse, Margaret Flowers]
Generated description
Margaret Flowers is known as the wife of the late MIT professor and robotics education pioneer Woodie Flowers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Margaret Flowers Target entity description: Margaret Flowers is known as the wife of the late MIT professor and robotics education pioneer Woodie Flowers.
-
A.
Margaret Gibson
Margaret Gibson was the wife of American actor Noah Beery, associated with the early Hollywood film era.
-
B.
Margaret Tucker
Margaret Tucker was an Aboriginal Australian activist and one of the country’s first female Indigenous authors, known for her pioneering work in civil rights and welfare for Aboriginal people.
-
C.
Margaret Whitmore
Margaret Whitmore is best known as the wife of American physician-turned-thriller novelist Robin Cook.
-
D.
Margaret Ann Dixon
Margaret Ann Dixon is best known as the wife of acclaimed Canadian film director and producer Norman Jewison.
-
E.
Marjorie Fowler
Marjorie Fowler was an American film editor known for her work on numerous Hollywood productions from the 1950s through the 1970s.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ad9831f88190a2b64cf6bc8c9a11 |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ca1b0bc2108190bdb3915c0fd94ff1 |
completed | March 30, 2026, 6:41 a.m. |
| NEDg | Description generation | batch_69ca1b7dd1b081908e9250f0dcb6d111 |
completed | March 30, 2026, 6:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ca1bcd488c8190a7c7c39ca51d199d |
completed | March 30, 2026, 6:44 a.m. |
Created at: March 27, 2026, 1:45 p.m.