Triple
T10556092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | coronation of Nicholas II of Russia |
E249089
|
entity |
| Predicate | numberOfGuestsApproximate |
P43282
|
FINISHED |
| Object | 7000 |
—
|
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: 7000 | Statement: [coronation of Nicholas II of Russia, numberOfGuestsApproximate, 7000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGuestsApproximate Context triple: [coronation of Nicholas II of Russia, numberOfGuestsApproximate, 7000]
-
A.
guestCountApproximate
chosen
Indicates that the number of guests involved is represented as an estimated or approximate count rather than an exact figure.
-
B.
passengersCountApproximate
Indicates that the number of passengers involved is given as an approximate or estimated count rather than an exact figure.
-
C.
numberOfPersons
Indicates the total count of individual persons associated with or involved in a given entity, event, or context.
-
D.
estimatedMemberCount
Indicates the approximate or predicted number of members associated with an entity.
-
E.
approximateNumberOfRooms
Indicates an estimated or not precisely known count of rooms associated with an entity.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d52712a9988190bf63e7c47f6e6fc1 |
completed | April 7, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69d518fa0b4081909bffc936d78bd77b |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:35 p.m.