Triple
T20444875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marshall Grant |
E501491
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marshall |
—
|
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: Marshall | Statement: [Marshall Grant, givenName, Marshall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marshall Context triple: [Marshall Grant, givenName, Marshall]
-
A.
Marshall
Marshall is a common English surname borne by numerous notable figures, including military leaders, politicians, and artists.
-
B.
Marshall
Marshall is a 2017 biographical legal drama film about a young Thurgood Marshall, directed by Reginald Hudlin and starring Chadwick Boseman.
-
C.
Marshall
chosen
Marshall is a masculine given name of English origin commonly used in the United States and other English-speaking countries.
-
D.
Marshall
Marshall is a small unincorporated coastal community in Marin County, California, known for its oyster farms along the eastern shore of Tomales Bay.
-
E.
Marshall
Marshall is a small East Texas city known for its historic architecture, role in the Civil War era, and cultural institutions such as Wiley College.
- 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_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68cfca4788190ad57ecb504f54d11 |
completed | April 20, 2026, 8:30 p.m. |
Created at: April 16, 2026, 11:32 a.m.