Triple
T7168157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Anschutz |
E167123
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Sarah
Sarah Anschutz is an individual whose given name is Sarah, likely a member of or associated with the prominent Anschutz family.
|
E651086
|
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: [Sarah Anschutz, givenName, Sarah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sarah Context triple: [Sarah Anschutz, givenName, Sarah]
-
A.
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.
-
B.
Sarah
Sarah is the central protagonist of the story "Horse Girl," around whom the main narrative and character development revolve.
-
C.
Sarah
Sarah is a member of the 2nd Massachusetts Militia Regiment, a historical military unit associated with the state of Massachusetts.
-
D.
Sarah
Sarah is the given first name of American actress and model Margaret Qualley, known for roles in projects like "Maid" and "Once Upon a Time in Hollywood."
-
E.
Sarah
Sarah is a fictional character from the 1992 British comedy-drama film "Peter’s Friends," which follows a group of Cambridge university friends reuniting after a decade.
- 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: [Sarah Anschutz, givenName, Sarah]
Generated description
Sarah Anschutz is an individual whose given name is Sarah, likely a member of or associated with the prominent Anschutz family.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sarah Target entity description: Sarah Anschutz is an individual whose given name is Sarah, likely a member of or associated with the prominent Anschutz family.
-
A.
Sarah
Sarah is the given first name of American actress and model Margaret Qualley, known for roles in projects like "Maid" and "Once Upon a Time in Hollywood."
-
B.
Sarah
Sarah is the first name of Sally Jewell, the former U.S. Secretary of the Interior and business executive.
-
C.
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.
-
D.
Sarah
Sarah is the given name of the legendary American jazz singer Sarah Vaughan, renowned for her rich vocal tone and improvisational skill.
-
E.
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.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e85b4410819098c6531229da51d4 |
completed | March 27, 2026, 8:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cbdab96c81909b9cfa10973fbf23 |
completed | March 28, 2026, 12:38 p.m. |
| NEDg | Description generation | batch_69c7cc86efa481909b0d6cb04755efde |
completed | March 28, 2026, 12:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7cd1d5ac08190bb085530c647a54c |
completed | March 28, 2026, 12:44 p.m. |
Created at: March 27, 2026, 2:48 p.m.