Triple
T11548210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York World's Fair (1939–1940) |
E273823
|
entity |
| Predicate | hadInternationalParticipants |
P69835
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [New York World's Fair (1939–1940), hadInternationalParticipants, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadInternationalParticipants Context triple: [New York World's Fair (1939–1940), hadInternationalParticipants, yes]
-
A.
hadInternationalComponentIn
chosen
Indicates that an event, activity, or entity included a significant international element or involvement during a specified time or context.
-
B.
hasParticipantCity
Indicates that a city is involved as a participant in an event, activity, or relationship.
-
C.
hasParticipants
Indicates that an event, activity, or situation involves one or more entities as participants in it.
-
D.
hasInternationalCommission
Indicates that an entity is associated with, overseen by, or participates in an international commission.
-
E.
hasGlobalParticipation
Indicates that an entity is involved in or contributes to activities, initiatives, or operations that span multiple countries or have worldwide scope.
- 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_69d6aae4dfa48190a3ab0b19a159a3c5 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d886e615b08190a072924329a94a6a |
completed | April 10, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69d8087cbe7c819085680f3d67ccc978 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:37 p.m.