Triple
T12442375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baldwin of Luxembourg |
E297306
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Baldwin |
E693952
|
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: Baldwin | Statement: [Baldwin of Luxembourg, givenName, Baldwin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baldwin Context triple: [Baldwin of Luxembourg, givenName, Baldwin]
-
A.
Baldwin
Baldwin is a common English surname of Anglo-Saxon origin that has been borne by numerous notable figures in literature, politics, and entertainment.
-
B.
Baldwin
Baldwin is a small rural community located within the town of Georgina in Ontario, Canada.
-
C.
Baldwin
Baldwin is a small township and rural community located within Ontario’s Sudbury District in Canada.
-
D.
Baldwin
chosen
Baldwin is a masculine given name of Germanic origin that has been borne by various European nobles and historical figures.
-
E.
Mr. Baldwin
Mr. Baldwin is a fictional character appearing in Evelyn Waugh’s satirical novel "Scoop."
- 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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d8ecb6c8190a19cbf9de31cabbd |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f10926881909ffc641f8d19f93a |
completed | May 2, 2026, 6:14 p.m. |
Created at: April 8, 2026, 9:55 p.m.