Triple
T9010530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madeleine |
E215457
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Madaline |
E772670
|
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: Madaline | Statement: [Madeleine, hasVariant, Madaline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madaline Context triple: [Madeleine, hasVariant, Madaline]
-
A.
Madelaine
Madelaine is a character in the Danish crime thriller film "The Salvation."
-
B.
Mignon
Mignon is a mysterious, ethereal child of Italian origin who becomes one of the most poignant and symbolically rich figures in Goethe’s novel "Wilhelm Meister’s Apprenticeship."
-
C.
Madeline
chosen
Madeline is a feminine given name, often considered a variant of Madeleine, commonly used in English-speaking countries.
-
D.
Madama
Madama is a Palestinian village located in the Nablus Governorate in the northern West Bank.
-
E.
Muriel
Muriel is a feminine given name of French origin that has been borne by various notable figures, including politicians, writers, and artists.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69c1571881908d0b144786b5ee1f |
completed | April 1, 2026, 12:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfeb671e488190920fb1780e4ad48d |
completed | April 3, 2026, 4:31 p.m. |
Created at: March 30, 2026, 7:06 p.m.