Triple
T2769348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Upshur Woodward |
E61414
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Diana Woodward
Diana Woodward is a daughter of renowned American investigative journalist Bob Woodward.
|
E297847
|
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: Diana Woodward | Statement: [Robert Upshur Woodward, hasChild, Diana Woodward]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diana Woodward Context triple: [Robert Upshur Woodward, hasChild, Diana Woodward]
-
A.
Diane McAfee
Diane McAfee is an American singer and actress best known as the mother of acclaimed musician Fiona Apple.
-
B.
Rachel Ward
Rachel Ward is a mathematician known for her influential research in applied and computational mathematics, including work in areas such as optimization and data science.
-
C.
Fiona Hill
Fiona Hill is a British-American foreign policy expert and former U.S. National Security Council official known for her expertise on Russia and her testimony in the first Trump impeachment inquiry.
-
D.
Margaret Sixel
Margaret Sixel is an Academy Award–winning film editor best known for her dynamic, high-intensity work on action films such as Mad Max: Fury Road.
-
E.
Susan Durant
Susan Durant was a prominent 19th-century English sculptor known for her portrait busts and association with notable Victorian figures.
- 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: Diana Woodward Triple: [Robert Upshur Woodward, hasChild, Diana Woodward]
Generated description
Diana Woodward is a daughter of renowned American investigative journalist Bob Woodward.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Diana Woodward Target entity description: Diana Woodward is a daughter of renowned American investigative journalist Bob Woodward.
-
A.
Diane McAfee
Diane McAfee is an American singer and actress best known as the mother of acclaimed musician Fiona Apple.
-
B.
Rachel Ward
Rachel Ward is a mathematician known for her influential research in applied and computational mathematics, including work in areas such as optimization and data science.
-
C.
Fiona Hill
Fiona Hill is a British-American foreign policy expert and former U.S. National Security Council official known for her expertise on Russia and her testimony in the first Trump impeachment inquiry.
-
D.
Margaret Sixel
Margaret Sixel is an Academy Award–winning film editor best known for her dynamic, high-intensity work on action films such as Mad Max: Fury Road.
-
E.
Susan Durant
Susan Durant was a prominent 19th-century English sculptor known for her portrait busts and association with notable Victorian figures.
- 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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd6785d88190b99f99889463a962 |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc04dc7808190bb8425ed87cf74c0 |
completed | March 10, 2026, 6:55 a.m. |
| NEDg | Description generation | batch_69afc11fa5ec8190a0b495f6b71591ab |
completed | March 10, 2026, 6:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc1f734dc8190a4861b05583f7018 |
completed | March 10, 2026, 7:02 a.m. |
Created at: March 6, 2026, 9:57 p.m.