Triple
T35110906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neuilly sa mère ! |
E1013283
|
entity |
| Predicate | boxOfficeFranceAdmissions |
P135769
|
FINISHED |
| Object | over 2 million |
—
|
LITERAL 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: over 2 million | Statement: [Neuilly sa mère !, boxOfficeFranceAdmissions, over 2 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: boxOfficeFranceAdmissions Context triple: [Neuilly sa mère !, boxOfficeFranceAdmissions, over 2 million]
-
A.
boxOfficeAdmissions
chosen
Indicates the number of tickets sold for a film or event, reflecting how many people attended via paid admissions.
-
B.
infrastructureManagerFrance
Indicates that an entity serves as the manager or operator responsible for infrastructure within France.
-
C.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
D.
reservedToFrance
Indicates that something is exclusively allocated or designated for France.
-
E.
hostStatusOfFrance
Indicates the hosting status or role that France holds in a given context or event.
- F. None of above.
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_69f76dd659d08190bcdc00d37caafb62 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78ce78b508190955848e133398dc8 |
completed | May 3, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69f78b8f4cc08190b49fccd798cb25d7 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 3, 2026, 4:01 p.m.