Triple
T2981756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cimetière des Batignolles |
E80527
|
entity |
| Predicate | hasGraveOf |
P196
|
FINISHED |
| Object | Gaston Leroux |
E243904
|
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: Gaston Leroux | Statement: [Cimetière des Batignolles, hasGraveOf, Gaston Leroux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gaston Leroux Context triple: [Cimetière des Batignolles, hasGraveOf, Gaston Leroux]
-
A.
Gaston Leroux
chosen
Gaston Leroux was a French journalist and novelist best known as the author of the classic mystery novel "The Phantom of the Opera."
-
B.
Jean-Baptiste Marie Gautier
Jean-Baptiste Marie Gautier was a French painter active in the 18th century who was associated with the prestigious Académie royale de peinture et de sculpture in Paris.
-
C.
Henri Harpignies
Henri Harpignies was a 19th-century French landscape painter known for his lyrical, atmospheric scenes and association with the Barbizon school.
-
D.
Raspail
Raspail is a Paris Métro station in the 14th arrondissement that serves as an interchange between lines 4 and 6.
-
E.
Henri Roussel
Henri Roussel is a French film critic and journalist known for his contributions to cinema commentary and analysis.
- 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_69ad8b15f6ac8190be5fd16a33edcb4f |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99a098e08190976eb4b019818f67 |
completed | March 8, 2026, 3:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108f27d648190a7a58670fec8b74d |
completed | March 11, 2026, 6:17 a.m. |
Created at: March 8, 2026, 2:58 p.m.