Triple
T1913395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lou |
E38160
|
entity |
| Predicate | shortFormOf |
P43
|
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: [Lou, shortFormOf, Louise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louise Context triple: [Lou, shortFormOf, 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.
Marie
Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
-
C.
Emilie
Emilie is a young French girl in Michael Morpurgo’s novel and its film adaptation "War Horse," who befriends and cares for the horses Joey and Topthorn during World War I.
-
D.
Madeleine
Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
-
E.
Jeanne
Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
- 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_69a8862a26088190aae5243695aeefc0 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1e26b948190aa194c30755ac5df |
completed | March 7, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6ae4225081909ea0e59a5885cfc3 |
completed | March 9, 2026, 6:38 a.m. |
Created at: March 4, 2026, 7:35 p.m.