Triple
T7526529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aloisia |
E177906
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Louise |
E5411
|
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: Louise | Statement: [Aloisia, relatedTo, Louise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louise Context triple: [Aloisia, relatedTo, Louise]
-
A.
Louise
chosen
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
B.
Georgette
Georgette is a comic servant character in Molière’s play "L’École des femmes," known for her earthy wit and role in highlighting the play’s social and gender tensions.
-
C.
Émilie
Émilie is the given first name of the French-born American actress Claudette Colbert, a major Hollywood star of the 1930s and 1940s.
-
D.
Isabelle
Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
-
E.
Marie
Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
- 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_69c69f29bf3081909a146aec7755f185 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f81b431481908214b69c6c8d83bc |
completed | March 27, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84efebd7081908fd33580b399a39b |
completed | March 28, 2026, 9:58 p.m. |
Created at: March 27, 2026, 3:47 p.m.