Triple
T1095199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenesaw Mountain Landis |
E24255
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Landis
Landis is a surname most famously associated with Kenesaw Mountain Landis, the first Commissioner of Major League Baseball known for his role in restoring public confidence after the Black Sox Scandal.
|
E127213
|
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: Landis | Statement: [Kenesaw Mountain Landis, familyName, Landis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Landis Context triple: [Kenesaw Mountain Landis, familyName, Landis]
-
A.
Stoffels
Stoffels is the surname of Hendrickje Stoffels, best known as the partner and model of the Dutch painter Rembrandt van Rijn.
-
B.
Heinsohn
Heinsohn is a surname most prominently associated with Tom Heinsohn, a Hall of Fame Boston Celtics player, coach, and broadcaster.
-
C.
Dellner
Dellner is a company specializing in railway coupling and connection systems used on modern passenger and freight trains worldwide.
-
D.
Busch
Busch is a common German surname borne by numerous notable individuals across fields such as politics, the arts, and industry.
-
E.
Gage
Gage is a surname of English origin borne by various notable individuals, including historical and contemporary figures.
- 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: Landis Triple: [Kenesaw Mountain Landis, familyName, Landis]
Generated description
Landis is a surname most famously associated with Kenesaw Mountain Landis, the first Commissioner of Major League Baseball known for his role in restoring public confidence after the Black Sox Scandal.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Landis Target entity description: Landis is a surname most famously associated with Kenesaw Mountain Landis, the first Commissioner of Major League Baseball known for his role in restoring public confidence after the Black Sox Scandal.
-
A.
Stoffels
Stoffels is the surname of Hendrickje Stoffels, best known as the partner and model of the Dutch painter Rembrandt van Rijn.
-
B.
Heinsohn
Heinsohn is a surname most prominently associated with Tom Heinsohn, a Hall of Fame Boston Celtics player, coach, and broadcaster.
-
C.
Dellner
Dellner is a company specializing in railway coupling and connection systems used on modern passenger and freight trains worldwide.
-
D.
Busch
Busch is a common German surname borne by numerous notable individuals across fields such as politics, the arts, and industry.
-
E.
Gage
Gage is a surname of English origin borne by various notable individuals, including historical and contemporary figures.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b99e92308190b8a8c499e1630672 |
completed | March 1, 2026, 10:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4c3bb31881908768a909ce56a95d |
completed | March 7, 2026, 4:03 p.m. |
| NEDg | Description generation | batch_69ac5020f5748190b89c938240e63637 |
completed | March 7, 2026, 4:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac50a982748190964d4fbef332baa5 |
completed | March 7, 2026, 4:22 p.m. |
Created at: March 1, 2026, 7:42 p.m.