Triple
T11429730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Marines under Robert E. Lee |
E270846
|
entity |
| Predicate | notableMember |
P10
|
FINISHED |
| Object |
Israel Greene
Israel Greene was a U.S. Marine Corps officer best known for leading the Marines’ assault that captured abolitionist John Brown at Harpers Ferry in 1859.
|
E924918
|
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: Israel Greene | Statement: [United States Marines under Robert E. Lee, notableMember, Israel Greene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Israel Greene Context triple: [United States Marines under Robert E. Lee, notableMember, Israel Greene]
-
A.
Tamir Greenwood
Tamir Greenwood is a child of Radiohead guitarist and composer Jonny Greenwood.
-
B.
Martin Greenberg
Martin Greenberg was an American publisher and editor best known for co-founding Gnome Press, which helped popularize early science fiction literature.
-
C.
Daniel Green
Daniel Green is a music producer known for his work on the track "Paradise."
-
D.
Jeffrey Greenstein
Jeffrey Greenstein is a film producer known for his work on action and genre movies, including the war drama "The Outpost."
-
E.
Joshua Goldstein
Joshua Goldstein is a political scientist best known for his influential work on international relations, war and peace studies, and global security.
- 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: Israel Greene Triple: [United States Marines under Robert E. Lee, notableMember, Israel Greene]
Generated description
Israel Greene was a U.S. Marine Corps officer best known for leading the Marines’ assault that captured abolitionist John Brown at Harpers Ferry in 1859.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Israel Greene Target entity description: Israel Greene was a U.S. Marine Corps officer best known for leading the Marines’ assault that captured abolitionist John Brown at Harpers Ferry in 1859.
-
A.
Tamir Greenwood
Tamir Greenwood is a child of Radiohead guitarist and composer Jonny Greenwood.
-
B.
Martin Greenberg
Martin Greenberg was an American publisher and editor best known for co-founding Gnome Press, which helped popularize early science fiction literature.
-
C.
Daniel Green
Daniel Green is a music producer known for his work on the track "Paradise."
-
D.
Jeffrey Greenstein
Jeffrey Greenstein is a film producer known for his work on action and genre movies, including the war drama "The Outpost."
-
E.
Joshua Goldstein
Joshua Goldstein is a political scientist best known for his influential work on international relations, war and peace studies, and global security.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c1bfb881909720c74fe0fa837f |
completed | April 9, 2026, 8:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b8d923688190bb4d61d57768e10e |
completed | April 20, 2026, 5:25 a.m. |
| NEDg | Description generation | batch_69e5c28e2dd481909b45a43b5825f393 |
completed | April 20, 2026, 6:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5c4722c348190a4c49edb1f6df240 |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:35 p.m.