Triple
T16904873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Marshall |
E424534
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Marshall |
E16065
|
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: Marshall | Statement: [Mary Marshall, familyName, Marshall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marshall Context triple: [Mary Marshall, familyName, Marshall]
-
A.
Marshall
Marshall is a 2017 biographical legal drama film about a young Thurgood Marshall, directed by Reginald Hudlin and starring Chadwick Boseman.
-
B.
Marshall
Marshall is one of the Adirondack High Peaks in New York’s Adirondack Mountains, known for its remote location and challenging, often trailless ascent.
-
C.
Marshall
Marshall is a masculine given name of English origin commonly used in the United States and other English-speaking countries.
-
D.
Marshall
chosen
Marshall is a common English surname borne by numerous notable figures, including military leaders, politicians, and artists.
-
E.
Marshall
Marshall is the enthusiastic Dalmatian fire-pup and medic from the PAW Patrol franchise, known for his clumsiness, big heart, and heroic rescues.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3c8df454c8190898ebdd75985e51c |
completed | April 18, 2026, 6:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7b7b698819081d2e39976b69587 |
completed | May 10, 2026, 6 p.m. |
Created at: April 10, 2026, 5:30 a.m.