Triple
T493081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1915 National League pennant |
E10230
|
entity |
| Predicate | seasonRecordLosses |
P14131
|
FINISHED |
| Object | 62 |
—
|
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: 62 | Statement: [1915 National League pennant, seasonRecordLosses, 62]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seasonRecordLosses Context triple: [1915 National League pennant, seasonRecordLosses, 62]
-
A.
careerLosses
Indicates the total number of defeats or losses an entity has accumulated over the course of its entire career.
-
B.
bestSeasonRecord
Indicates that one entity holds the best (most successful) season performance record among a set of entities, typically in a competitive or statistical context.
-
C.
loserScore
Indicates the number of points or score achieved by the losing participant in a competitive event or comparison.
-
D.
consecutiveWinsInSeason
Indicates that one entity achieved a specified number of back-to-back victories within a single season.
-
E.
loserPoints
Indicates the number of points awarded to or accumulated by the losing side in a competitive event or comparison.
- 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.