Triple
T697044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomeryshire |
E13915
|
entity |
| Predicate | hasMajorTown |
P316
|
FINISHED |
| Object |
Montgomery
Montgomery is a historic market town in Powys, Wales, known for its medieval castle ruins and Georgian architecture.
|
E109567
|
NE FINISHED |
How this triple was built (4 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: Montgomery | Statement: [Montgomeryshire, hasMajorTown, Montgomery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montgomery Context triple: [Montgomeryshire, hasMajorTown, Montgomery]
-
A.
Montgomery
Montgomery is a common English and Scottish surname of Norman origin, historically associated with nobility and military figures.
-
B.
Montgomery, Alabama
Montgomery, Alabama is the state capital known as a pivotal center of the American civil rights movement, including events such as the Montgomery Bus Boycott.
-
C.
Gadsden, Alabama
Gadsden, Alabama is a small industrial city in northeastern Alabama known historically for its manufacturing plants and labor history.
-
D.
Bessemer, Alabama
Bessemer, Alabama is an industrial city in Jefferson County that forms part of the greater Birmingham region in central Alabama.
-
E.
Tuscaloosa, Alabama
Tuscaloosa, Alabama is a city in western Alabama known as the home of the University of Alabama and a regional center for education, healthcare, and industry.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Montgomery Triple: [Montgomeryshire, hasMajorTown, Montgomery]
Generated description
Montgomery is a historic market town in Powys, Wales, known for its medieval castle ruins and Georgian architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Montgomery Target entity description: Montgomery is a historic market town in Powys, Wales, known for its medieval castle ruins and Georgian architecture.
-
A.
Montgomery
Montgomery is a common English and Scottish surname of Norman origin, historically associated with nobility and military figures.
-
B.
Montgomery, Alabama
Montgomery, Alabama is the state capital known as a pivotal center of the American civil rights movement, including events such as the Montgomery Bus Boycott.
-
C.
Gadsden, Alabama
Gadsden, Alabama is a small industrial city in northeastern Alabama known historically for its manufacturing plants and labor history.
-
D.
Bessemer, Alabama
Bessemer, Alabama is an industrial city in Jefferson County that forms part of the greater Birmingham region in central Alabama.
-
E.
Tuscaloosa, Alabama
Tuscaloosa, Alabama is a city in western Alabama known as the home of the University of Alabama and a regional center for education, healthcare, and industry.
- F. None of above. chosen
Provenance (5 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_69a493406c408190957eeec9048a8fb6 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a0c8055881909565ebde2be8fd7a |
completed | March 1, 2026, 8:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7edf6b02c8190995c50a98b4ec326 |
completed | March 4, 2026, 8:31 a.m. |
| NEDg | Description generation | batch_69a7f1cc03b48190a291ca9c20646648 |
completed | March 4, 2026, 8:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7f26264d48190ad04ee855523fcc1 |
completed | March 4, 2026, 8:50 a.m. |
Created at: March 1, 2026, 7:36 p.m.