Triple
T12384260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gérard de Battista |
E295820
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Monsieur Ibrahim |
E29833
|
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: Monsieur Ibrahim | Statement: [Gérard de Battista, notableWork, Monsieur Ibrahim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monsieur Ibrahim Context triple: [Gérard de Battista, notableWork, Monsieur Ibrahim]
-
A.
Monsieur Ibrahim
chosen
Monsieur Ibrahim is a 2003 French drama film in which Omar Sharif delivers an acclaimed performance as a wise Turkish shopkeeper who befriends a lonely Parisian boy.
-
B.
Chérif
Chérif is a family name of Arabic origin historically associated with notable North African figures such as Ahmed Bey ben Mohamed Chérif.
-
C.
Mr. Arabin
Mr. Arabin is a clergyman and academic who becomes a central romantic interest in Anthony Trollope’s novel "Barchester Towers."
-
D.
Mounir
Mounir is a masculine given name of Arabic origin, commonly used in various Arabic-speaking and Muslim-majority countries.
-
E.
Saïd
Saïd is a masculine given name of Arabic origin, commonly used in various forms across the Middle East and North Africa.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fbc3f608190b0ee3c4f304a94db |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62ac78c5c81909fb3d63bc6c9cc01 |
completed | May 2, 2026, 4:48 p.m. |
Created at: April 8, 2026, 9:54 p.m.