Triple
T14320670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir William Rouse Ball |
E355076
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ball |
E49180
|
NE 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: Ball | Statement: [Sir William Rouse Ball, familyName, Ball]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ball Context triple: [Sir William Rouse Ball, familyName, Ball]
-
A.
Ball
chosen
Ball is a common English surname borne by numerous individuals, including the famed American comedian and actress Lucille Ball.
-
B.
Ball
Ball is a well-known consumer brand, particularly recognized for its glass canning jars and home food preservation products.
-
C.
Palla
Palla is a Portuguese surname most notably associated with journalist and feminist activist Maria Antónia Palla.
-
D.
Bola
Bola is the given name of Bola Tinubu, a prominent Nigerian politician and current president of Nigeria.
-
E.
Boule
Boule was the citizen council of Classical Athens responsible for preparing legislation and overseeing the day-to-day administration of the city-state’s government.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8278ed42c8190b9f882dcce611347 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de883bf71c8190a9a092a025cf98f0 |
completed | April 14, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd468c4a7c8190951adb28e1d71365 |
completed | May 8, 2026, 2:12 a.m. |
Created at: April 10, 2026, 1:13 a.m.