Triple
T10056345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake İznik |
E208871
|
entity |
| Predicate | usedByNearbyTownFor |
P91868
|
FINISHED |
| Object | local recreation |
—
|
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: local recreation | Statement: [Lake İznik, usedByNearbyTownFor, local recreation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedByNearbyTownFor Context triple: [Lake İznik, usedByNearbyTownFor, local recreation]
-
A.
usedInTown
Indicates that something is utilized, applied, or functions within the context or boundaries of a particular town.
-
B.
hasNearbyTown
Indicates that one location has a town situated close to it in geographic proximity.
-
C.
hasNearbyTownType
Indicates that one entity has, in its vicinity, a town of a specified type or classification.
-
D.
usedInCity
Indicates that something is utilized, applied, or operates within the context or boundaries of a particular city.
-
E.
hasTownCommon
Indicates that a place possesses or includes a town common, i.e., a shared public open space traditionally used by the local community.
- 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_69ca836094408190a36a1ea7e9a86fcd |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcfae503881909b9f016da4e2207d |
completed | April 2, 2026, 2:08 a.m. |
| PD | Predicate disambiguation | batch_69cd4b92573481909389bc6148ae7ea8 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 8:57 p.m.