Triple
T32022512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louvre INV 1980 |
E817731
|
entity |
| Predicate | inventoryNumberOfCollection |
P116155
|
FINISHED |
| Object | Louvre Museum |
—
|
NE NERFINISHED |
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: Louvre Museum | Statement: [Louvre INV 1980, inventoryNumberOfCollection, Louvre Museum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inventoryNumberOfCollection Context triple: [Louvre INV 1980, inventoryNumberOfCollection, Louvre Museum]
-
A.
inventoryNumberOf
chosen
Indicates that one entity is the inventory or catalog number assigned to another entity.
-
B.
collectionSize
Indicates the total number of items contained within a specified collection.
-
C.
inventoryNumberAt
Indicates that a specific inventory number is assigned to an entity at a particular location or point in time.
-
D.
collectionVolume
Indicates the total physical or spatial amount occupied by a collection, such as its size, capacity, or volume.
-
E.
bookNumberInCollection
Indicates the specific numerical position assigned to a book within a particular collection.
- 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_69f348fb04e4819081f4eab040ed7959 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:17 a.m.