Triple
T390058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suzanne Farrell |
E8860
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Ficker
Ficker is the birth surname of renowned American ballerina Suzanne Farrell, one of the most celebrated muses of choreographer George Balanchine.
|
E49208
|
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: Ficker | Statement: [Suzanne Farrell, familyName, Ficker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ficker Context triple: [Suzanne Farrell, familyName, Ficker]
-
A.
Foege
Foege is the surname of William H. Foege, an American epidemiologist renowned for his pivotal role in the global eradication of smallpox.
-
B.
Freeman
Freeman Dyson was a renowned theoretical physicist and mathematician known for his work in quantum electrodynamics, solid-state physics, and futurist writings.
-
C.
Fink
Fink is an open-source package management system that brings a wide range of Unix and open-source software to macOS by compiling and distributing it in a convenient, Debian-like format.
-
D.
Milhous
Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
-
E.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
- 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: Ficker Triple: [Suzanne Farrell, familyName, Ficker]
Generated description
Ficker is the birth surname of renowned American ballerina Suzanne Farrell, one of the most celebrated muses of choreographer George Balanchine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ficker Target entity description: Ficker is the birth surname of renowned American ballerina Suzanne Farrell, one of the most celebrated muses of choreographer George Balanchine.
-
A.
Foege
Foege is the surname of William H. Foege, an American epidemiologist renowned for his pivotal role in the global eradication of smallpox.
-
B.
Freeman
Freeman Dyson was a renowned theoretical physicist and mathematician known for his work in quantum electrodynamics, solid-state physics, and futurist writings.
-
C.
Fink
Fink is an open-source package management system that brings a wide range of Unix and open-source software to macOS by compiling and distributing it in a convenient, Debian-like format.
-
D.
Milhous
Milhous is the distinctive middle name of Richard Nixon, the 37th president of the United States.
-
E.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec5bdc848190826701590070497b |
completed | Feb. 28, 2026, 1:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4035310608190a1e0f807cb93e1e1 |
completed | March 1, 2026, 9:13 a.m. |
| NEDg | Description generation | batch_69a403a7c1488190a7773a5ae8a8cec7 |
completed | March 1, 2026, 9:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a40428b014819091c6534ba35a11ff |
completed | March 1, 2026, 9:17 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.