Triple
T2234537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cecilia Suyat Marshall |
E49248
|
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: [Cecilia Suyat Marshall, familyName, Marshall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marshall Context triple: [Cecilia Suyat Marshall, familyName, Marshall]
-
A.
Marshall
chosen
Marshall is a common English surname borne by numerous notable figures, including military leaders, politicians, and artists.
-
B.
Warren
Warren is a common English surname borne by numerous notable figures in politics, law, entertainment, and other fields.
-
C.
Warren
Warren is the given name of Warren Buffett, the renowned American investor and longtime CEO of Berkshire Hathaway.
-
D.
Warren
Warren is a large suburban city in southeast Michigan known for its extensive automotive and defense manufacturing industries.
-
E.
Randolph
Randolph is a character from Truman Capote’s Southern Gothic novel "Other Voices, Other Rooms," notable for his eccentric, theatrical personality and complex, ambiguous sexuality.
- 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_69a88aa84bdc819086df50e9c20b301e |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc09297a481909e8fe6ec645de616 |
completed | March 7, 2026, 6:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6b068a388190962114f3798ef4f9 |
completed | March 9, 2026, 6:39 a.m. |
Created at: March 4, 2026, 7:47 p.m.