Triple
T31825522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LeUyen Pham |
E812382
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Alvin Ho series
The Alvin Ho series is a humorous children's book collection about a second-grade boy with anxiety, written by Lenore Look and illustrated by LeUyen Pham.
|
E1980394
|
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: Alvin Ho series | Statement: [LeUyen Pham, notableWork, Alvin Ho series]
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: Alvin Ho series Triple: [LeUyen Pham, notableWork, Alvin Ho series]
Generated description
The Alvin Ho series is a humorous children's book collection about a second-grade boy with anxiety, written by Lenore Look and illustrated by LeUyen Pham.
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_69f348e97fa48190aa06286962af6dee |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6af83b9bc8190bfc09358c876928a |
completed | May 3, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2e659eba3881909e51cdc73614611b |
completed | June 14, 2026, 8:26 a.m. |
| NEDg | Description generation | batch_6a2e6754a848819092c9658e0837fecd |
completed | June 14, 2026, 8:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2e698816ac81909f66c1323f766eaa |
completed | June 14, 2026, 8:42 a.m. |
Created at: April 30, 2026, 11:46 p.m.