Triple
T38349024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maykopsky District (de facto, without being part of it) |
E1041624
|
entity |
| Predicate | usesNearbyCityForAdministration |
P104638
|
FINISHED |
| Object | Maykop |
—
|
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: Maykop | Statement: [Maykopsky District (de facto, without being part of it), usesNearbyCityForAdministration, Maykop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesNearbyCityForAdministration Context triple: [Maykopsky District (de facto, without being part of it), usesNearbyCityForAdministration, Maykop]
-
A.
hasNearbyCityFunction
chosen
Indicates that one entity serves as a nearby urban center or city-like service hub for another entity.
-
B.
hasNearbyCityArea
Indicates that one area is geographically close to or adjacent to a city area.
-
C.
hasMunicipalitySeatNearby
Indicates that the municipality’s administrative seat is located in close proximity to the referenced place or entity.
-
D.
hasNearbyUSCity
Indicates that one location has at least one city in the United States situated within a specified nearby distance.
-
E.
isMunicipalityNear
Indicates that one municipality is located close to another municipality or geographic reference point.
- 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_69f76e2ad95481908c920c0e5c1c3e26 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fcc7a4d7f881908b43b960911b81e9 |
completed | May 7, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69fcc589720c819089c8f500fea3c86a |
completed | May 7, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:30 p.m.