Triple
T14889831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleanor Copenhaver |
E359723
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Copenhaver
Copenhaver is a surname of likely English or German origin borne by various individuals, including Eleanor Copenhaver.
|
E1125787
|
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: Copenhaver | Statement: [Eleanor Copenhaver, familyName, Copenhaver]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Copenhaver Context triple: [Eleanor Copenhaver, familyName, Copenhaver]
-
A.
Sydhavn
Sydhavn is a district in Copenhagen, Denmark, known for its former industrial harbor areas now undergoing redevelopment into residential and commercial neighborhoods.
-
B.
Amaliehaven
Amaliehaven is a small waterfront park and fountain garden in central Copenhagen, known for its formal design and views of the harbor and Amalienborg Palace.
-
C.
Hankø
Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
-
D.
Christiansted
Christiansted is a historic coastal town on the island of Saint Croix in the U.S. Virgin Islands, known for its preserved Danish colonial architecture and waterfront.
-
E.
Kastrup
Kastrup is a district in the Tårnby Municipality near Copenhagen, Denmark, best known for hosting the country’s main international airport.
- 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: Copenhaver Triple: [Eleanor Copenhaver, familyName, Copenhaver]
Generated description
Copenhaver is a surname of likely English or German origin borne by various individuals, including Eleanor Copenhaver.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Copenhaver Target entity description: Copenhaver is a surname of likely English or German origin borne by various individuals, including Eleanor Copenhaver.
-
A.
Sydhavn
Sydhavn is a district in Copenhagen, Denmark, known for its former industrial harbor areas now undergoing redevelopment into residential and commercial neighborhoods.
-
B.
Amaliehaven
Amaliehaven is a small waterfront park and fountain garden in central Copenhagen, known for its formal design and views of the harbor and Amalienborg Palace.
-
C.
Hankø
Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
-
D.
Christiansted
Christiansted is a historic coastal town on the island of Saint Croix in the U.S. Virgin Islands, known for its preserved Danish colonial architecture and waterfront.
-
E.
Kastrup
Kastrup is a district in the Tårnby Municipality near Copenhagen, Denmark, best known for hosting the country’s main international airport.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded5f6cf5c8190b6b28f58fafe5d59 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b61407481908a618d14c56d2abf |
completed | May 8, 2026, 11:01 p.m. |
| NEDg | Description generation | batch_69fe6e21bdf481908dba4b745ed4be65 |
completed | May 8, 2026, 11:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe6ee69860819096a2448ab813dc1d |
completed | May 8, 2026, 11:16 p.m. |
Created at: April 10, 2026, 2:09 a.m.