Triple
T10081561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeanne Langevin |
E213911
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Jeanne |
E191141
|
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: Jeanne | Statement: [Jeanne Langevin, hasGivenName, Jeanne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeanne Context triple: [Jeanne Langevin, hasGivenName, Jeanne]
-
A.
Jeanne
chosen
Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
-
B.
Renée
Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
-
C.
Jeanne Carmen
Jeanne Carmen was an American model, pin-up girl, and B-movie actress known for her roles in low-budget films of the 1950s and her colorful Hollywood social life.
-
D.
Jean
Jean is a given name associated here with Georges Cuvier, the influential French naturalist and zoologist who founded the field of comparative anatomy and helped establish extinction as a scientific fact.
-
E.
Jean
Jean is a central character in the action-thriller film "Executive Decision," involved in the high-stakes mission to thwart a terrorist hijacking.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd03482d481908b03d35dc2d16395 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b660987c8190a6a29d9e56acbff7 |
completed | April 5, 2026, 7:22 p.m. |
Created at: March 30, 2026, 9 p.m.