Triple
T23689016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2005–06 La Liga |
E585247
|
entity |
| Predicate | fewestLossesTeam |
P153405
|
FINISHED |
| Object | FC Barcelona |
—
|
NE NERFINISHED |
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: FC Barcelona | Statement: [2005–06 La Liga, fewestLossesTeam, FC Barcelona]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fewestLossesTeam Context triple: [2005–06 La Liga, fewestLossesTeam, FC Barcelona]
-
A.
fewestGoalsTeam
Indicates the team that has conceded or scored the lowest number of goals compared to all other teams in the relevant context.
-
B.
mostLossesTeam
Indicates the team that has incurred the greatest number of losses within a given set of teams or season.
-
C.
fewestGoalsConcededRecordScope
Indicates the specific context or scope (such as competition, season, or time period) within which a record for the fewest goals conceded is defined.
-
D.
mostAwardsHolderTeam
Indicates the team that holds the highest number of awards within a given set or competition.
-
E.
worstTeamPoints
Indicates the number of points earned by the lowest-performing or worst-ranked team in a given context or competition.
- 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_69e249037ce0819088b149608e98f685 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b5c0a218819096c2d174b004c47d |
completed | April 29, 2026, 7:39 a.m. |
| PD | Predicate disambiguation | batch_69f155d5265881908e43a9696b6a6d0f |
completed | April 29, 2026, 12:50 a.m. |
| PDg | Predicate description generation | batch_69f157cc43a881909ed2d8b0a09b5d73 |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 17, 2026, 6:52 p.m.