Triple
T17591149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Greenwood |
E428449
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Greenwood |
—
|
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: Greenwood | Statement: [Christopher Greenwood, familyName, Greenwood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greenwood Context triple: [Christopher Greenwood, familyName, Greenwood]
-
A.
Greenwood
Greenwood is a residential neighborhood within the city of Wakefield in Middlesex County, Massachusetts.
-
B.
Greenwood
Greenwood is a small village located in McHenry County, Illinois, United States.
-
C.
Greenwood
Greenwood is a residential neighborhood within the town of Bicester in Oxfordshire, England.
-
D.
Greenwood
chosen
Greenwood is a common English surname borne by numerous notable individuals across fields such as music, sports, and politics.
-
E.
Greenwood
Greenwood is a small city in the Mississippi Delta region of the United States, historically known as a center of cotton production and civil rights–era activity.
- 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e6e3888190b73a5b6d7e8c0a55 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.