Triple
T10866394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Embassy |
E256540
|
entity |
| Predicate | numberOfEnvoys |
P10538
|
FINISHED |
| Object | approximately 250 |
—
|
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: approximately 250 | Statement: [Grand Embassy, numberOfEnvoys, approximately 250]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfEnvoys Context triple: [Grand Embassy, numberOfEnvoys, approximately 250]
-
A.
numberOfDelegates
chosen
Indicates the quantity of delegates associated with or assigned to a particular entity or event.
-
B.
hasAmbassador
Indicates that one entity serves as an official diplomatic representative (ambassador) for another entity.
-
C.
numberOfConsulships
Indicates the total count of times an entity has held the office or role of consul.
-
D.
numberOfExpeditions
Indicates the total count of expeditions associated with a given entity or context.
-
E.
numberOfColoniesRepresented
Indicates the count of distinct colonies that are represented or involved in relation to 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7516cebe881909ed358a7641f6a12 |
completed | April 9, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69d70d308dfc81908792f98cfb871392 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:20 p.m.