Triple
T16786455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Blau DuPlessis |
E407991
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Rachel
Rachel is the given name of the American poet, critic, and feminist scholar Rachel Blau DuPlessis.
|
E1234848
|
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: Rachel | Statement: [Rachel Blau DuPlessis, givenName, Rachel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Context triple: [Rachel Blau DuPlessis, givenName, Rachel]
-
A.
Rachel
Rachel is a prominent biblical matriarch in the Book of Genesis, known as Jacob’s beloved wife and the mother of Joseph and Benjamin.
-
B.
Rachel
Rachel is the famous bronze piggy bank sculpture and unofficial mascot of Seattle’s Pike Place Market, known for collecting donations for local social services.
-
C.
Rachel
Rachel is a central protagonist in the science fiction television series "The Starlost," playing a key role in the story’s exploration of a generation ship and its isolated communities.
-
D.
Rachel
Rachel is a central character in Michael Ondaatje’s novel "Warlight," whose mysterious past and complex relationships drive much of the story’s intrigue and emotional tension.
-
E.
Rachel
Rachel is the central character in the psychological thriller miniseries "Behind Her Eyes," portrayed by actress Simona Brown.
- 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: Rachel Triple: [Rachel Blau DuPlessis, givenName, Rachel]
Generated description
Rachel is the given name of the American poet, critic, and feminist scholar Rachel Blau DuPlessis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rachel Target entity description: Rachel is the given name of the American poet, critic, and feminist scholar Rachel Blau DuPlessis.
-
A.
Rachel
Rachel is the given name of Rachel Carson, the influential American marine biologist and conservationist whose writings advanced the global environmental movement.
-
B.
Rachel
Rachel is a prominent biblical matriarch in the Book of Genesis, known as Jacob’s beloved wife and the mother of Joseph and Benjamin.
-
C.
Rachel
Rachel is a feminine given name of Hebrew origin meaning "ewe," historically associated with the biblical matriarch and widely used in many cultures.
-
D.
Rachel
Rachel is a fictional character portrayed by French actress Clémence Poésy, known for her roles in film and television dramas.
-
E.
Rachel
Rachel is a central character in Michael Ondaatje’s novel "Warlight," whose mysterious past and complex relationships drive much of the story’s intrigue and emotional tension.
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b21a52ac8190b4374aa0fc45683a |
completed | April 18, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b287a96c8190a16d7c76d05be106 |
completed | May 10, 2026, 4:29 p.m. |
| NEDg | Description generation | batch_6a00b3dd883081908d97dc81c4891145 |
completed | May 10, 2026, 4:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00b466ecd08190b7b5ee54476631ab |
completed | May 10, 2026, 4:37 p.m. |
Created at: April 10, 2026, 5:22 a.m.