Triple
T2129958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Fuller |
E46514
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Sarah
Sarah is the birth name of Margaret Fuller, the 19th-century American journalist, critic, and women's rights advocate associated with the Transcendentalist movement.
|
E273162
|
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: Sarah | Statement: [Margaret Fuller, givenName, Sarah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Context triple: [Margaret Fuller, givenName, Sarah]
-
A.
Sarah
Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
-
B.
Jessica
Jessica is a kind-hearted schoolteacher who becomes Mrs. Claus in the classic stop-motion Christmas special "Santa Claus Is Comin' to Town."
-
C.
Jessica
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
-
D.
Anna
Anna is the given name of pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
-
E.
Anna
Anna is a spirited and optimistic princess from Disney's animated film "Frozen," known for her bravery, loyalty, and deep love for her sister Elsa.
- 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: Sarah Triple: [Margaret Fuller, givenName, Sarah]
Generated description
Sarah is the birth name of Margaret Fuller, the 19th-century American journalist, critic, and women's rights advocate associated with the Transcendentalist movement.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sarah Target entity description: Sarah is the birth name of Margaret Fuller, the 19th-century American journalist, critic, and women's rights advocate associated with the Transcendentalist movement.
-
A.
Sarah
Sarah is a key matriarch in the Hebrew Bible, revered as the wife of Abraham and mother of Isaac in the Jewish, Christian, and Islamic traditions.
-
B.
Jessica
Jessica is a kind-hearted schoolteacher who becomes Mrs. Claus in the classic stop-motion Christmas special "Santa Claus Is Comin' to Town."
-
C.
Jessica
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
-
D.
Anna
Anna is the given name of pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
-
E.
Anna
Anna is a spirited and optimistic princess from Disney's animated film "Frozen," known for her bravery, loyalty, and deep love for her sister Elsa.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbb77ccc4819087bee5dbb91b5ae8 |
completed | March 7, 2026, 5:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f72ad8c81909da734f6316f747f |
completed | March 9, 2026, 7:28 p.m. |
| NEDg | Description generation | batch_69af21e9efd08190829bd7c4b1c50695 |
completed | March 9, 2026, 7:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af22d6a7d4819093640b2797167c46 |
completed | March 9, 2026, 7:43 p.m. |
Created at: March 4, 2026, 7:44 p.m.