Triple
T409741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bobby Charlton |
E9462
|
entity |
| Predicate | nationalTeamGoals |
P9098
|
FINISHED |
| Object | 49 for England |
—
|
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: 49 for England | Statement: [Bobby Charlton, nationalTeamGoals, 49 for England]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalTeamGoals Context triple: [Bobby Charlton, nationalTeamGoals, 49 for England]
-
A.
nationalFootballTeam
Indicates that one entity is the official football (soccer) team representing the other entity at the national level.
-
B.
nationalCaps
Indicates that one entity serves as the national capital city of another entity (typically a country or nation).
-
C.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
D.
mostTeamsInCountry
Indicates that an entity has the highest number of teams located within a given country compared to all other entities.
-
E.
team2Country
Indicates that a given team is associated with or represents a particular country.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ed31681c8190ac32334562fb17fd |
completed | Feb. 28, 2026, 1:27 p.m. |
| PD | Predicate disambiguation | batch_69a2e9737694819080fde9adcc1aa4d4 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.