Triple
T5708934
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Farrow |
E125855
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Farrow
Farrow is the surname of American actress and humanitarian Mia Farrow, known for her work in film and activism.
|
E542583
|
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: Farrow | Statement: [Mia Farrow, familyName, Farrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Farrow Context triple: [Mia Farrow, familyName, Farrow]
-
A.
Farley
Farley is a rural-residential suburb in the Maitland region of New South Wales, Australia.
-
B.
Farley
Farley is a surname most notably associated with Jim Farley, an American business executive and CEO of Ford Motor Company.
-
C.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
-
D.
Farah Stockman
Farah Stockman is an American journalist and New York Times editorial board member known for her incisive commentary on politics, race, and social justice.
-
E.
Fanny Hayes
Fanny Hayes was the daughter of U.S. President Rutherford B. Hayes and First Lady Lucy Webb Hayes, who grew up in the White House during her father's administration.
- 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: Farrow Triple: [Mia Farrow, familyName, Farrow]
Generated description
Farrow is the surname of American actress and humanitarian Mia Farrow, known for her work in film and activism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Farrow Target entity description: Farrow is the surname of American actress and humanitarian Mia Farrow, known for her work in film and activism.
-
A.
Farley
Farley is a rural-residential suburb in the Maitland region of New South Wales, Australia.
-
B.
Farley
Farley is a surname most notably associated with Jim Farley, an American business executive and CEO of Ford Motor Company.
-
C.
Fay
Fay is a given name most famously associated with Canadian-American actress Fay Wray, the iconic star of the 1933 film "King Kong."
-
D.
Farah Stockman
Farah Stockman is an American journalist and New York Times editorial board member known for her incisive commentary on politics, race, and social justice.
-
E.
Fanny Hayes
Fanny Hayes was the daughter of U.S. President Rutherford B. Hayes and First Lady Lucy Webb Hayes, who grew up in the White House during her father's administration.
- 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_69c0082d6fe48190b777fb383769e5c8 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0248ab6a88190be17bdc32c36e5cb |
completed | March 22, 2026, 5:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a6f5ac08190b5acbccea756d2de |
completed | March 22, 2026, 9:09 p.m. |
| NEDg | Description generation | batch_69c062029e3c8190ade3f0836d6b3842 |
completed | March 22, 2026, 9:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c06268eb0c8190959ba762c2d9b47d |
completed | March 22, 2026, 9:43 p.m. |
Created at: March 22, 2026, 3:46 p.m.