Triple
T21283278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Roberts |
E524585
|
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 Roberts, givenName, Rachel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Context triple: [Rachel Roberts, givenName, Rachel]
-
A.
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.
-
B.
Rachel
chosen
Rachel is a feminine given name of Hebrew origin meaning "ewe," historically associated with the biblical matriarch and widely used in many cultures.
-
C.
Rachel
Rachel is a fictional character portrayed by French actress Clémence Poésy, known for her roles in film and television dramas.
-
D.
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.
-
E.
Rachel
Rachel is a central character in the contemporary romance novel "Kissing Lessons," involved in a story of teenage relationships, self-discovery, and emotional growth.
- 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_69e0b5171f6c8190a5d57201ede73811 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e736d3dfbc819081bd876d95c7c480 |
completed | April 21, 2026, 8:35 a.m. |
Created at: April 16, 2026, 4:03 p.m.