Triple
T35473840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Machinery Hall |
E1025271
|
entity |
| Predicate | cityHostedEvent |
P142838
|
FINISHED |
| Object | Chicago |
—
|
NE NERFINISHED |
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: Chicago | Statement: [Machinery Hall, cityHostedEvent, Chicago]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityHostedEvent Context triple: [Machinery Hall, cityHostedEvent, Chicago]
-
A.
countryHostedEvent
Indicates that a specific country served as the host location for a particular event.
-
B.
associatedCityEvent
Indicates a relationship where a city is linked to, involved in, or serves as the location for a particular event.
-
C.
cityHostedAgain
Indicates that a city has hosted a particular event on more than one occasion, including the current instance.
-
D.
hostedAttraction
chosen
Indicates that an entity served as the venue or location where a particular attraction, event, or feature was presented or took place.
-
E.
hostsEvent
Indicates that an entity organizes and provides the venue or setting for an event to take place.
- 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_69f76dfadba0819083456aadcd6864ea |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:04 p.m.