Triple
T9199132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marjorie Tallchief |
E220791
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marjorie |
E235982
|
NE FINISHED |
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: Marjorie | Statement: [Marjorie Tallchief, givenName, Marjorie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marjorie Context triple: [Marjorie Tallchief, givenName, Marjorie]
-
A.
Marjorie
chosen
Marjorie is a feminine given name of French origin that has been widely used in English-speaking countries.
-
B.
Geraldine
Geraldine is a feminine given name of Germanic origin that has been borne by various notable figures, including actress Geraldine Chaplin.
-
C.
Geraldine
Geraldine is a small rural service town in the South Island of New Zealand, known for its scenic surroundings and role as a gateway to the Canterbury high country.
-
D.
Mary Ruth
Mary Ruth is a fictional character featured in the American television sitcom "The Debbie Reynolds Show."
-
E.
Margaret Avery
Margaret Avery is an American actress best known for her Academy Award–nominated performance as Shug Avery in the film adaptation of "The Color Purple."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca83e8e9248190862cf3e41693b310 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd880ab808190aa785a5f1e5d5976 |
completed | April 1, 2026, 8:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d077808ee48190be6b58a4b38f3e3d |
completed | April 4, 2026, 2:29 a.m. |
Created at: March 30, 2026, 7:25 p.m.