Triple
T16576198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boxwood Hall |
E402716
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object |
Elizabeth
Elizabeth is a historic city in northeastern New Jersey, known as one of the state’s oldest urban centers and a key part of the New York metropolitan area.
|
E55624
|
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: Elizabeth | Statement: [Boxwood Hall, city, Elizabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Context triple: [Boxwood Hall, city, Elizabeth]
-
A.
Elizabeth
Elizabeth is the middle name of Diane Elizabeth Dern, an individual likely known in relation to the Dern family.
-
B.
Elizabeth
Elizabeth is the birth name of American actress and singer Betty Hutton, a popular Hollywood star of the 1940s and 1950s.
-
C.
Elizabeth
Elizabeth is the given first name of American actress Bess Armstrong, known for her work in film and television since the late 1970s.
-
D.
Elizabeth
Elizabeth is the middle name of Tipper Gore, the American social issues advocate and former Second Lady of the United States.
-
E.
Elizabeth
Elizabeth was the birth name of Princess Elizabeth of England, who later became Queen Elizabeth I, the influential Tudor monarch of England.
- 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: Elizabeth Triple: [Boxwood Hall, city, Elizabeth]
Generated description
Elizabeth is a historic city in northeastern New Jersey, known as one of the state’s oldest urban centers and a key part of the New York metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elizabeth Target entity description: Elizabeth is a historic city in northeastern New Jersey, known as one of the state’s oldest urban centers and a key part of the New York metropolitan area.
-
A.
Elizabeth
chosen
Elizabeth is a city in northeastern New Jersey that forms part of the greater New York metropolitan area.
-
B.
Elizabeth
Elizabeth is a small town in Colorado known for its rural character and proximity to the Denver metropolitan area.
-
C.
Elizabeth
Elizabeth is a suburban electorate in South Australia known for its working-class community and industrial heritage within the northern Adelaide region.
-
D.
Elizabeth
Elizabeth is a nearby town or community adjacent to Hillside, likely sharing local amenities and regional connections.
-
E.
Elizabeth
Elizabeth is the full given name of Betsy McCaughey, an American politician, writer, and former lieutenant governor of New York.
- F. None of above.
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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3595cb65481909be62a52deff3d44 |
completed | April 18, 2026, 10:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006ee8812c81908ef74636bf39d44a |
completed | May 10, 2026, 11:41 a.m. |
| NEDg | Description generation | batch_6a0070024cb4819092ee0ce1320f0905 |
completed | May 10, 2026, 11:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00707959a081909fc04947624abbe5 |
completed | May 10, 2026, 11:48 a.m. |
Created at: April 10, 2026, 5:16 a.m.