Triple
T34586422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | First Purchase African M.E. Church |
E888054
|
entity |
| Predicate | setInTownType |
P163391
|
FINISHED |
| Object | small Southern town |
—
|
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: small Southern town | Statement: [First Purchase African M.E. Church, setInTownType, small Southern town]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInTownType Context triple: [First Purchase African M.E. Church, setInTownType, small Southern town]
-
A.
isTown
Indicates that the subject entity is classified as a town.
-
B.
situatedInTown
chosen
Indicates that one entity is located within the geographical or administrative boundaries of a specific town.
-
C.
hasTown
Indicates that one entity possesses, contains, or is associated with a town as part of its structure, jurisdiction, or composition.
-
D.
fromTown
Indicates that one entity originates from, or is associated as being from, a particular town represented by the other entity.
-
E.
isInteriorTownOf
Indicates that one town is located within the interior region of, and is administratively or geographically associated with, a larger area or jurisdiction.
- 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_69f349d25cbc8190869998de5915886b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f720c8e74c819084f402fb9f935513 |
completed | May 3, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_69f71cc8074c81909ae09bea2acf1a09 |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 2:03 a.m.