Triple
T36332999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1. FC Saarbrücken |
E894702
|
entity |
| Predicate | womenTeamPlayedLeague |
P141828
|
FINISHED |
| Object | Frauen-Bundesliga |
—
|
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: Frauen-Bundesliga | Statement: [1. FC Saarbrücken, womenTeamPlayedLeague, Frauen-Bundesliga]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: womenTeamPlayedLeague Context triple: [1. FC Saarbrücken, womenTeamPlayedLeague, Frauen-Bundesliga]
-
A.
hasWomenTeamInLeague
chosen
Indicates that an entity has a women’s team that participates in a specified league.
-
B.
womenTeam
Indicates that the team is composed of women or is designated as a women’s team.
-
C.
featuresWomenTeams
Indicates that the subject includes, presents, or involves teams composed of women.
-
D.
womenTeamsCount
Indicates the number of teams composed of women associated with a given entity or context.
-
E.
includesWomen'sLeagues
Indicates that the subject entity encompasses, offers, or is associated with one or more leagues specifically organized for women.
- 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_69f76e4dcf088190a6c3216c209cab52 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bcccd7988190aa5c931ff347d33c |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:09 p.m.