Triple
T38126401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houghton Lake |
E952084
|
entity |
| Predicate | Tip Up Town USAIs |
P81625
|
FINISHED |
| Object | annual winter festival |
—
|
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: annual winter festival | Statement: [Houghton Lake, Tip Up Town USAIs, annual winter festival]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: Tip Up Town USAIs Context triple: [Houghton Lake, Tip Up Town USAIs, annual winter festival]
-
A.
isTown
Indicates that the subject entity is classified as a town.
-
B.
fromTown
Indicates that one entity originates from, or is associated as being from, a particular town represented by the other entity.
-
C.
hasTown
Indicates that one entity possesses, contains, or is associated with a town as part of its structure, jurisdiction, or composition.
-
D.
associatedRealWorldTown
Indicates a relationship where an entity is linked or connected to a specific real-world town.
-
E.
hostTown
chosen
Indicates that one entity is the town or municipality that hosts, contains, or serves as the location for the other entity.
- 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_69f76f083548819082bd2bbf53c79e8e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fc4748843c8190931432653be4890c |
completed | May 7, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69fc45646ce481908caf292ff9f06e15 |
completed | May 7, 2026, 7:55 a.m. |
Created at: May 3, 2026, 4:21 p.m.