Triple
T5785990
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lewis Chessmen |
E128269
|
entity |
| Predicate | numberOfItemsAtLocation |
P59623
|
FINISHED |
| Object | 82 at the British Museum |
—
|
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: 82 at the British Museum | Statement: [Lewis Chessmen, numberOfItemsAtLocation, 82 at the British Museum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfItemsAtLocation Context triple: [Lewis Chessmen, numberOfItemsAtLocation, 82 at the British Museum]
-
A.
collectionSize
Indicates the total number of items contained within a specified collection.
-
B.
numberOfStandingPlaces
Indicates the total count of standing-only positions or spots available in a given context (e.g., a vehicle, venue, or area).
-
C.
numberOfPositions
Indicates the total count of distinct positions or roles associated with a given entity.
-
D.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
E.
numberOfEntries
chosen
Indicates the total count of individual items, records, or instances associated with a given entity or context.
- 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_69c0084450048190bc647b649a05136b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02a1af0ec8190a47be1b7e7b5cda7 |
completed | March 22, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69c021d2cd608190b98a7e3aa7001d27 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:51 p.m.