Triple
T20916335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel de Queiroz |
E515082
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rachel |
—
|
NE NERFINISHED |
How this triple was built (2 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 de Queiroz, givenName, Rachel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Context triple: [Rachel de Queiroz, givenName, Rachel]
-
A.
Rachel
Rachel is a central protagonist in the Australian television drama series "The Newsreader," which follows the turbulent personal and professional lives of 1980s broadcast journalists.
-
B.
Rachel
chosen
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 the famous bronze piggy bank sculpture and unofficial mascot of Seattle’s Pike Place Market, known for collecting donations for local social services.
-
D.
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.
-
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.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4f9d5ec8190bb2bd27350ed341c |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6ec628a38819093dcb70de91c770b |
completed | April 21, 2026, 3:17 a.m. |
Created at: April 16, 2026, 12:48 p.m.