Triple
T493084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1915 National League pennant |
E10230
|
entity |
| Predicate | pennantClinchedDecade |
P14132
|
FINISHED |
| Object | 1910s |
—
|
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: 1910s | Statement: [1915 National League pennant, pennantClinchedDecade, 1910s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pennantClinchedDecade Context triple: [1915 National League pennant, pennantClinchedDecade, 1910s]
-
A.
endedInDecade
Indicates that an event, process, or state concluded during a specified decade.
-
B.
leaguePennant
Indicates that a team has won the championship pennant for a particular league or season.
-
C.
emergedInDecade
Indicates that something first appeared, originated, or came into prominence during a specified decade.
-
D.
pennantTeam
Indicates that a team has won or represents the league or conference pennant for a given season or competition.
-
E.
mostPennantsTeam
Indicates that the subject is the team that has won the greatest number of league pennants within a specified context or league.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f0faab4881909f65f172198b5bd2 |
completed | Feb. 28, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69a2edf7ce008190836fb6ab5ea39375 |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eebb2c908190960a4d0c014304cd |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.