Triple
T102030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | World Series |
E2058
|
entity |
| Predicate | hasHistoricalInterruptions |
P6409
|
FINISHED |
| Object | labor disputes |
—
|
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: labor disputes | Statement: [World Series, hasHistoricalInterruptions, labor disputes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricalInterruptions Context triple: [World Series, hasHistoricalInterruptions, labor disputes]
-
A.
hasHistoricalEvent
Indicates that a historical event occurred in, is associated with, or is relevant to a particular entity.
-
B.
isHistoric
Indicates that something has significant importance or relevance in history, often due to its age, impact, or role in past events.
-
C.
hasHistoricalPrecursor
Indicates that one entity existed earlier and served as a predecessor, model, or influential forerunner to the other in a historical context.
-
D.
hasHistoricalContext
Indicates that something is related to, influenced by, or best understood in light of specific past events, conditions, or time periods.
-
E.
historicalState
Indicates that an entity existed in a particular state or condition during a specified time in the past.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25760af348190bf402089c240887d |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a2563921f8819087f720b1c803579f |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a2575d8a648190ad8e10d4b04e5e07 |
completed | Feb. 28, 2026, 2:47 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.