Triple
T417713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beekman |
E8030
|
entity |
| Predicate | isInCountySeatOf |
P14649
|
FINISHED |
| Object | none |
—
|
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: none | Statement: [Beekman, isInCountySeatOf, none]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInCountySeatOf Context triple: [Beekman, isInCountySeatOf, none]
-
A.
countySeat
Indicates that one place serves as the administrative center or capital of a county.
-
B.
hasMetropolitanCounty
Indicates that an entity is associated with, located within, or administered by a specific metropolitan county.
-
C.
includesCounty
Indicates that a larger geographic or administrative region contains or encompasses a specific county within its boundaries.
-
D.
isUrbanCounty
Indicates that a county is classified as urban, typically based on population density, development level, or similar urbanization criteria.
-
E.
countyName
Indicates the specific name assigned to a county in which an entity is located or with which it is associated.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edd1ca148190a66bd8c5aad867d5 |
completed | Feb. 28, 2026, 1:29 p.m. |
| PDg | Predicate description generation | batch_69a2eeb8545c8190a2b8517e7ed5b92e |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.