Triple
T13648208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Girl in Landscape |
E326658
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
David Marsh
David Marsh is a fictional character featured in the novel "Girl in Landscape."
|
E1056156
|
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: David Marsh | Statement: [Girl in Landscape, hasCharacter, David Marsh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Marsh Context triple: [Girl in Landscape, hasCharacter, David Marsh]
-
A.
Michael Marshall
Michael Marshall is an Anglican clergyman who served as the Bishop of Woolwich in the Church of England.
-
B.
Alan Marshall
Alan Marshall is a British film producer known for his work on notable movies including the musical gangster film "Bugsy Malone."
-
C.
Alan Manning
Alan Manning is a British labour economist and professor at the London School of Economics, known for his influential research on wage inequality, monopsony in labour markets, and immigration policy.
-
D.
David Richards
David Richards was a British record producer and audio engineer best known for his work with bands like Queen and artists such as David Bowie, particularly at Mountain Studios in Montreux.
-
E.
Michael Harnett
Michael Harnett is the birth name of Michael Hartnett, a prominent Irish poet known for his lyrical work in both English and Irish.
- 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: David Marsh Triple: [Girl in Landscape, hasCharacter, David Marsh]
Generated description
David Marsh is a fictional character featured in the novel "Girl in Landscape."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: David Marsh Target entity description: David Marsh is a fictional character featured in the novel "Girl in Landscape."
-
A.
Michael Marshall
Michael Marshall is an Anglican clergyman who served as the Bishop of Woolwich in the Church of England.
-
B.
Alan Marshall
Alan Marshall is a British film producer known for his work on notable movies including the musical gangster film "Bugsy Malone."
-
C.
Alan Manning
Alan Manning is a British labour economist and professor at the London School of Economics, known for his influential research on wage inequality, monopsony in labour markets, and immigration policy.
-
D.
David Richards
David Richards was a British record producer and audio engineer best known for his work with bands like Queen and artists such as David Bowie, particularly at Mountain Studios in Montreux.
-
E.
Michael Harnett
Michael Harnett is the birth name of Michael Hartnett, a prominent Irish poet known for his lyrical work in both English and Irish.
- 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_69d8076d8270819092afc2f0e9c359a8 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc6073e888190965456a639839749 |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7943610488190838719ad31207c52 |
completed | May 3, 2026, 6:30 p.m. |
| NEDg | Description generation | batch_69f7955fce288190a7e426f467517a91 |
completed | May 3, 2026, 6:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7996cddf08190973e493fb788ce7a |
completed | May 3, 2026, 6:52 p.m. |
Created at: April 9, 2026, 9:52 p.m.