Triple
T21741089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabriel Batistuta |
E536659
|
entity |
| Predicate | totalInternationalGoals |
P145185
|
FINISHED |
| Object | 56 |
—
|
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: 56 | Statement: [Gabriel Batistuta, totalInternationalGoals, 56]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalInternationalGoals Context triple: [Gabriel Batistuta, totalInternationalGoals, 56]
-
A.
worldCupGoals
Indicates the number of goals an entity scored in World Cup matches.
-
B.
FIFAWorldCupGoalsRank
Indicates the ranking of entities based on the number of goals they have scored in FIFA World Cup competitions.
-
C.
topGoalScorerGoals
Indicates the number of goals scored by the top goal scorer in a given context or competition.
-
D.
rankAllTimeGoals
Indicates a relationship that orders entities based on the total number of goals they have scored across all time.
-
E.
internationalFootballCaps
Indicates the number of official international football (soccer) matches a player has appeared in for their national team.
- F. None of above. chosen
Provenance (4 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_69e0c46df5448190b4322127ffc4c690 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01a7250d48190aa63f89db017ef70 |
completed | April 28, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69e6969c16fc8190b5126c169317d85d |
completed | April 20, 2026, 9:11 p.m. |
| PDg | Predicate description generation | batch_69e69f3ed4408190a4a78410bf660c44 |
completed | April 20, 2026, 9:48 p.m. |
Created at: April 16, 2026, 6:49 p.m.