Triple
T37023073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Josef Masopust |
E916272
|
entity |
| Predicate | clubAppearancesForDuklaPrague |
P13108
|
FINISHED |
| Object | 386 |
—
|
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: 386 | Statement: [Josef Masopust, clubAppearancesForDuklaPrague, 386]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: clubAppearancesForDuklaPrague Context triple: [Josef Masopust, clubAppearancesForDuklaPrague, 386]
-
A.
clubAppearances
chosen
Indicates the number of official matches a player has played for a particular club.
-
B.
leagueAppearances
Indicates the number of times an entity has participated in official league matches or competitions.
-
C.
numberOfGamesInCzechoslovakia
Indicates the total count of games that took place in Czechoslovakia.
-
D.
associatedClubBosnianCups
Indicates that there is a relationship between an entity and one or more Bosnian Cup competitions with which its club is associated.
-
E.
clubNumberOfGoalsForBudapestHonved
Indicates the number of goals scored for the club Budapest Honvéd by a given player.
- 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_69f76e920dc48190acb6bb7ebc4dffab |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb154c0fe08190a2e41e7a29b6055f |
completed | May 6, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_69f9fecc005c8190be082a8689193745 |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 3, 2026, 4:14 p.m.