Triple
T15840505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Craig Lowndes |
E384088
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Lowndes
Lowndes is a surname most prominently associated with Australian racing driver Craig Lowndes.
|
E1179726
|
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: Lowndes | Statement: [Craig Lowndes, familyName, Lowndes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lowndes Context triple: [Craig Lowndes, familyName, Lowndes]
-
A.
Lowndes County
Lowndes County is a rural county in central Alabama known for its significant role in the American civil rights movement, particularly during the Selma to Montgomery marches.
-
B.
Troup
Troup is a surname of Scottish origin borne by various notable individuals, including politicians, artists, and public figures.
-
C.
Hale County
Hale County is a rural county in west-central Alabama known for its agricultural landscape, small towns, and role in the Black Belt region.
-
D.
Lamar County
Lamar County is a rural county in western Alabama known for its small communities and agricultural landscape.
-
E.
Lamar County
Lamar County is a county in northeastern Texas known for its seat in the city of Paris and its location near the Oklahoma border.
- 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: Lowndes Triple: [Craig Lowndes, familyName, Lowndes]
Generated description
Lowndes is a surname most prominently associated with Australian racing driver Craig Lowndes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lowndes Target entity description: Lowndes is a surname most prominently associated with Australian racing driver Craig Lowndes.
-
A.
Lowndes County
Lowndes County is a rural county in central Alabama known for its significant role in the American civil rights movement, particularly during the Selma to Montgomery marches.
-
B.
Troup
Troup is a surname of Scottish origin borne by various notable individuals, including politicians, artists, and public figures.
-
C.
Hale County
Hale County is a rural county in west-central Alabama known for its agricultural landscape, small towns, and role in the Black Belt region.
-
D.
Lamar County
Lamar County is a rural county in western Alabama known for its small communities and agricultural landscape.
-
E.
Lamar County
Lamar County is a county in northeastern Texas known for its seat in the city of Paris and its location near the Oklahoma border.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142e69360819091ea0556bd66d785 |
completed | April 16, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa13c931481908ed9fd10fddd867c |
completed | May 9, 2026, 9:03 p.m. |
| NEDg | Description generation | batch_69ffa419c6dc81908c9678f8434530f8 |
completed | May 9, 2026, 9:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffa4cff5088190a7f11fd62941f4fb |
completed | May 9, 2026, 9:19 p.m. |
Created at: April 10, 2026, 4:49 a.m.