Triple
T3083266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Arellano |
E64306
|
entity |
| Predicate | clubNumberOfGoals |
P9098
|
FINISHED |
| Object | prolific scorer for Colo-Colo |
—
|
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: prolific scorer for Colo-Colo | Statement: [David Arellano, clubNumberOfGoals, prolific scorer for Colo-Colo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clubNumberOfGoals Context triple: [David Arellano, clubNumberOfGoals, prolific scorer for Colo-Colo]
-
A.
numberOfGoals
chosen
Indicates the total count of goals scored or achieved by an entity in a given context.
-
B.
sportNumberOfClubs
Indicates the number of clubs or teams an entity is associated with in a sports context.
-
C.
totalGoalsRecord
Indicates the total number of goals that have been recorded for an entity across all relevant events or contexts.
-
D.
clubAppearances
Indicates the number of official matches a player has played for a particular club.
-
E.
teamAchievement
Indicates that a group of individuals collectively accomplished a shared goal or met a performance milestone.
- 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1e877008190aacbd6f1357bdb9b |
completed | March 8, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69ad9debb6308190be28378ae1fc98af |
completed | March 8, 2026, 4:03 p.m. |
Created at: March 8, 2026, 3:03 p.m.