Triple
T12048943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werner March |
E286861
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | March |
E789022
|
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: March | Statement: [Werner March, familyName, March]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: March Context triple: [Werner March, familyName, March]
-
A.
March
March is a fictional family surname most famously associated with the four sisters in Louisa May Alcott’s novel "Little Women."
-
B.
March
"March" is a critically acclaimed graphic memoir trilogy co-written by civil rights leader John Lewis and Andrew Aydin that chronicles Lewis's experiences in the American civil rights movement.
-
C.
March
chosen
March is a common surname of Spanish origin borne by various notable individuals, including Cuban revolutionary Aleida March.
-
D.
March
March is a market town in Cambridgeshire, England, known historically as an important railway and river port center in the Fens.
-
E.
March
March is the third month of the Gregorian calendar, marking the transition from winter to spring in the Northern Hemisphere and often associated with seasonal festivals and observances.
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d904227958819084dbd5eb2566c735 |
completed | April 10, 2026, 2:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49dc459308190bc88cb550e1d5b86 |
completed | May 1, 2026, 12:34 p.m. |
Created at: April 8, 2026, 9:47 p.m.