Triple
T1949658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R. H. Tawney |
E42129
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Tawney
Tawney is an English surname most notably associated with the influential economic historian and social critic R. H. Tawney.
|
E219748
|
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: Tawney | Statement: [R. H. Tawney, familyName, Tawney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tawney Context triple: [R. H. Tawney, familyName, Tawney]
-
A.
Leatherhead
Leatherhead is a historic market town in the county of Surrey in South East England, situated on the River Mole and serving as a local commercial and commuter hub.
-
B.
Bassett
Bassett is the surname of acclaimed American actress and director Angela Bassett, known for her powerful performances in film and television.
-
C.
Norris
Norris is a surname most notably associated with influential American politician George W. Norris, a progressive-era U.S. senator from Nebraska.
-
D.
Morris
Morris is the given first name of Moe Berg, the American Major League Baseball catcher who later became a World War II intelligence officer.
-
E.
Thirlby
Thirlby is the surname of Olivia Thirlby, an American actress known for roles in films such as "Juno" and "Dredd."
- 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: Tawney Triple: [R. H. Tawney, familyName, Tawney]
Generated description
Tawney is an English surname most notably associated with the influential economic historian and social critic R. H. Tawney.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tawney Target entity description: Tawney is an English surname most notably associated with the influential economic historian and social critic R. H. Tawney.
-
A.
Leatherhead
Leatherhead is a historic market town in the county of Surrey in South East England, situated on the River Mole and serving as a local commercial and commuter hub.
-
B.
Bassett
Bassett is the surname of acclaimed American actress and director Angela Bassett, known for her powerful performances in film and television.
-
C.
Norris
Norris is a surname most notably associated with influential American politician George W. Norris, a progressive-era U.S. senator from Nebraska.
-
D.
Morris
Morris is the given first name of Moe Berg, the American Major League Baseball catcher who later became a World War II intelligence officer.
-
E.
Thirlby
Thirlby is the surname of Olivia Thirlby, an American actress known for roles in films such as "Juno" and "Dredd."
- 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_69a8870e08fc8190a319cbf2600db15f |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb332b014819085bfc88d66cfc10a |
completed | March 7, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adfbc17ce08190bd74c9c62d260326 |
completed | March 8, 2026, 10:44 p.m. |
| NEDg | Description generation | batch_69adfc3d37e4819082673b84eb5a19f2 |
completed | March 8, 2026, 10:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adfd7ef5588190ab85f3981466d1e0 |
completed | March 8, 2026, 10:51 p.m. |
Created at: March 4, 2026, 7:36 p.m.