Triple
T26115314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis Sachar |
E658807
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Small Steps
Small Steps is a young adult novel by Louis Sachar that follows a teen recently released from a juvenile detention camp as he struggles to rebuild his life and make better choices.
|
E1711247
|
NE FINISHED |
How this triple was built (2 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: Small Steps | Statement: [Louis Sachar, notableWork, Small Steps]
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: Small Steps Triple: [Louis Sachar, notableWork, Small Steps]
Generated description
Small Steps is a young adult novel by Louis Sachar that follows a teen recently released from a juvenile detention camp as he struggles to rebuild his life and make better choices.
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_69ee5bc20298819099a42be042eb2349 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f60ac72ad88190b27d7de5409b1d3f |
completed | May 2, 2026, 2:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11275455f481909068b62470d8885c |
completed | May 23, 2026, 4:04 a.m. |
| NEDg | Description generation | batch_6a112d28f9c08190bf93215c0d97cc23 |
completed | May 23, 2026, 4:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a112e27e4b08190be06432034aa7912 |
completed | May 23, 2026, 4:33 a.m. |
Created at: April 26, 2026, 8:04 p.m.