Triple
T3002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herbert Hoover |
E56
|
entity |
| Predicate | middleName |
P143
|
FINISHED |
| Object |
Clark
Clark is the middle name of Herbert Hoover, the 31st president of the United States.
|
E2868
|
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: Clark | Statement: [Herbert Hoover, middleName, Clark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clark Context triple: [Herbert Hoover, middleName, Clark]
-
A.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
-
B.
Lawrence
Lawrence is a historic mill city in northeastern Massachusetts that developed as a major textile manufacturing center along the Merrimack River.
-
C.
Lexington
Lexington is a historic suburban town in Massachusetts, United States, best known as the site where the first shots of the American Revolutionary War were fired at the Battle of Lexington in 1775.
-
D.
Manchester
Manchester is a major city in northwest England known for its industrial heritage, vibrant cultural scene, and influential contributions to music, sport, and science.
-
E.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
- 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: Clark Triple: [Herbert Hoover, middleName, Clark]
Generated description
Clark is the middle name of Herbert Hoover, the 31st president of the United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Clark Target entity description: Clark is the middle name of Herbert Hoover, the 31st president of the United States.
-
A.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
-
B.
Lawrence
Lawrence is a historic mill city in northeastern Massachusetts that developed as a major textile manufacturing center along the Merrimack River.
-
C.
Lexington
Lexington is a historic suburban town in Massachusetts, United States, best known as the site where the first shots of the American Revolutionary War were fired at the Battle of Lexington in 1775.
-
D.
Manchester
Manchester is a major city in northwest England known for its industrial heritage, vibrant cultural scene, and influential contributions to music, sport, and science.
-
E.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
- 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_69a2328f0e848190ac2840eaf2d5ebd2 |
completed | Feb. 28, 2026, 12:10 a.m. |
| NER | Named-entity recognition | batch_69a233c52368819093215a9c745f264c |
completed | Feb. 28, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a24e51246c819098f53258d869ecde |
completed | Feb. 28, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_69a250b213c881909381abc66f5ebe68 |
completed | Feb. 28, 2026, 2:19 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2515b915c8190b122de0025bd954f |
completed | Feb. 28, 2026, 2:22 a.m. |
Created at: Feb. 28, 2026, 12:13 a.m.