Triple
T2425203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grzegorz Lato |
E53509
|
entity |
| Predicate | yearsActiveAsPlayer |
P8357
|
FINISHED |
| Object | 1960s–1980s |
—
|
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: 1960s–1980s | Statement: [Grzegorz Lato, yearsActiveAsPlayer, 1960s–1980s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsActiveAsPlayer Context triple: [Grzegorz Lato, yearsActiveAsPlayer, 1960s–1980s]
-
A.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
B.
activeYearsInSport
chosen
Indicates the span of years during which an entity actively participated in a particular sport.
-
C.
leagueParticipatedInAsPlayer
Indicates that a person, in the role of a player, took part in competitions organized by a specific league.
-
D.
activeYearsInWorldCup
Indicates the span of years during which an entity actively participated in World Cup competitions.
-
E.
numberOfNHLSeasons
Indicates the total count of seasons an entity has participated in the National Hockey League (NHL).
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc9f342e88190a430b02842ded418 |
completed | March 7, 2026, 6:47 a.m. |
| PD | Predicate disambiguation | batch_69abc5a889948190b77de4ef6ac815a8 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 6, 2026, 9:42 p.m.