Triple
T6883884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margriet Francisca |
E158866
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Margriet |
E17722
|
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: Margriet | Statement: [Margriet Francisca, givenName, Margriet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margriet Context triple: [Margriet Francisca, givenName, Margriet]
-
A.
Margaret
chosen
Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
-
B.
Margaret
Margaret is a 2011 American drama film written and directed by Kenneth Lonergan, known for its complex portrayal of grief and moral responsibility following a tragic bus accident in New York City.
-
C.
Marjorie
Marjorie is a feminine given name of French origin that has been widely used in English-speaking countries.
-
D.
Margriet Francisca
Margriet Francisca is a Dutch princess, the third daughter of former Queen Juliana and Prince Bernhard of the Netherlands, known for her public service and charitable work.
-
E.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
- 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_69c688342f6c8190ad7eea6ba262db99 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d8ea59108190a85f9112b6a4e59a |
completed | March 27, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c742d652088190a65e06eb7fe79cfc |
completed | March 28, 2026, 2:54 a.m. |
Created at: March 27, 2026, 2:23 p.m.