Triple
T14651136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babette Gladney |
E343986
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Babette |
E41151
|
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: Babette | Statement: [Babette Gladney, givenName, Babette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Babette Context triple: [Babette Gladney, givenName, Babette]
-
A.
Babette
chosen
Babette is a feminine given name, commonly used as a diminutive or variant of the name Barbara.
-
B.
Babette’s Feast
Babette’s Feast is a celebrated short story by Karen Blixen (Isak Dinesen) about a French refugee who transforms a strict Danish religious community through an extravagant, grace-filled meal.
-
C.
Babette Goes to War
Babette Goes to War is a 1959 French comedy film starring Brigitte Bardot as a young woman recruited for a World War II espionage mission.
-
D.
La Provençale
La Provençale is the section of France’s A8 motorway that runs across Provence, linking cities such as Aix-en-Provence, Cannes, and Nice along the Mediterranean coast.
-
E.
La Cotinière
La Cotinière is a traditional fishing port village on the Atlantic coast of France, known for its active fishing fleet and seafood market.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb517e7648190b9fc73d6cdbb68de |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5d9ab188190934deb57fd6d9a56 |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 10, 2026, 1:27 a.m.