Triple
T17412707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Smiles |
E423408
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Thrift
Thrift is a self-help and moral improvement book by Samuel Smiles that promotes industriousness, frugality, and personal responsibility as keys to individual and social progress.
|
E1267957
|
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: Thrift | Statement: [Samuel Smiles, notableWork, Thrift]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thrift Context triple: [Samuel Smiles, notableWork, Thrift]
-
A.
Thrift
Thrift is an open-source software framework for scalable cross-language services development, providing an interface definition language and code generation for efficient RPC and data serialization.
-
B.
Takas
Takas is a dialect of the Mwaghavul language spoken by a subgroup of the Mwaghavul people in Nigeria’s Plateau State.
-
C.
Tarifit
Tarifit is a Northern Berber language spoken primarily by the Riffian people in the Rif region of northern Morocco.
-
D.
Tweants
Tweants is a Low Saxon regional dialect spoken in the Twente region of the eastern Netherlands.
-
E.
Thumbs
"Thumbs" is a 2016 pop song by Sabrina Carpenter known for its rapid-fire lyrics and commentary on monotonous, conformist lifestyles.
- 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: Thrift Triple: [Samuel Smiles, notableWork, Thrift]
Generated description
Thrift is a self-help and moral improvement book by Samuel Smiles that promotes industriousness, frugality, and personal responsibility as keys to individual and social progress.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thrift Target entity description: Thrift is a self-help and moral improvement book by Samuel Smiles that promotes industriousness, frugality, and personal responsibility as keys to individual and social progress.
-
A.
Thrift
Thrift is an open-source software framework for scalable cross-language services development, providing an interface definition language and code generation for efficient RPC and data serialization.
-
B.
Takas
Takas is a dialect of the Mwaghavul language spoken by a subgroup of the Mwaghavul people in Nigeria’s Plateau State.
-
C.
Tarifit
Tarifit is a Northern Berber language spoken primarily by the Riffian people in the Rif region of northern Morocco.
-
D.
Tweants
Tweants is a Low Saxon regional dialect spoken in the Twente region of the eastern Netherlands.
-
E.
Thumbs
"Thumbs" is a 2016 pop song by Sabrina Carpenter known for its rapid-fire lyrics and commentary on monotonous, conformist lifestyles.
- 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_69d889d7d27c819088486ce3f0627fa1 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43b0c12b881908b2ddc13678c7a75 |
completed | April 19, 2026, 2:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01a7fdb4348190a905b476379c3245 |
completed | May 11, 2026, 9:57 a.m. |
| NEDg | Description generation | batch_6a01a886818881909f237f69c6d77f93 |
completed | May 11, 2026, 9:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01a919bd288190aed27a1523afc470 |
completed | May 11, 2026, 10:02 a.m. |
Created at: April 10, 2026, 5:46 a.m.