Triple
T1121650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Clair County, Alabama |
E24624
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Margaret, Alabama
Margaret, Alabama is a small city in central Alabama that forms part of the Birmingham–Hoover metropolitan area.
|
E196320
|
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: Margaret, Alabama | Statement: [St. Clair County, Alabama, hasTown, Margaret, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margaret, Alabama Context triple: [St. Clair County, Alabama, hasTown, Margaret, Alabama]
-
A.
Steele, Alabama
Steele, Alabama is a small town in northeastern Alabama known for its rural character and location within St. Clair County.
-
B.
Helena, Alabama
Helena, Alabama is a suburban city in central Alabama known for its family-friendly neighborhoods, strong schools, and proximity to Birmingham.
-
C.
Maylene, Alabama
Maylene, Alabama is an unincorporated community and suburban area located in central Alabama within Shelby County.
-
D.
Vincent, Alabama
Vincent, Alabama is a small town in central Alabama known for its rural character and location within the Birmingham–Hoover metropolitan area.
-
E.
Moody, Alabama
Moody, Alabama is a small suburban city in central Alabama that forms part of the Birmingham metropolitan area.
- 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: Margaret, Alabama Triple: [St. Clair County, Alabama, hasTown, Margaret, Alabama]
Generated description
Margaret, Alabama is a small city in central Alabama that forms part of the Birmingham–Hoover metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Margaret, Alabama Target entity description: Margaret, Alabama is a small city in central Alabama that forms part of the Birmingham–Hoover metropolitan area.
-
A.
Steele, Alabama
Steele, Alabama is a small town in northeastern Alabama known for its rural character and location within St. Clair County.
-
B.
Helena, Alabama
Helena, Alabama is a suburban city in central Alabama known for its family-friendly neighborhoods, strong schools, and proximity to Birmingham.
-
C.
Maylene, Alabama
Maylene, Alabama is an unincorporated community and suburban area located in central Alabama within Shelby County.
-
D.
Vincent, Alabama
Vincent, Alabama is a small town in central Alabama known for its rural character and location within the Birmingham–Hoover metropolitan area.
-
E.
Moody, Alabama
Moody, Alabama is a small suburban city in central Alabama that forms part of the Birmingham metropolitan area.
- 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bbbf71188190b82c8fff9d5ac01a |
completed | March 1, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0b527dc819085f7b3ff85170e16 |
completed | March 8, 2026, 4:15 p.m. |
| NEDg | Description generation | batch_69ada1e13d408190b393c00c331125a2 |
completed | March 8, 2026, 4:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ada292a34c8190a566c2909342ab27 |
completed | March 8, 2026, 4:23 p.m. |
Created at: March 1, 2026, 7:44 p.m.