Triple
T25007581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Talk to Girls at Parties (comic adaptation) |
E625888
|
entity |
| Predicate | artist |
P184
|
FINISHED |
| Object |
Fábio Moon
Fábio Moon is a Brazilian comic book artist and writer known for his collaborations with his twin brother Gabriel Bá on acclaimed graphic novels and adaptations.
|
E1656778
|
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: Fábio Moon | Statement: [How to Talk to Girls at Parties (comic adaptation), artist, Fábio Moon]
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: Fábio Moon Triple: [How to Talk to Girls at Parties (comic adaptation), artist, Fábio Moon]
Generated description
Fábio Moon is a Brazilian comic book artist and writer known for his collaborations with his twin brother Gabriel Bá on acclaimed graphic novels and adaptations.
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_69e2ff26c50481908bc82e799c9e6587 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f44b12bd788190bc32bb8129c4550e |
completed | May 1, 2026, 6:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1033759af08190b19c2d3c9ab6d66b |
completed | May 22, 2026, 10:44 a.m. |
| NEDg | Description generation | batch_6a103440175081908c16266d18fa3f7f |
completed | May 22, 2026, 10:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1034f2e0b88190b296a251056bce15 |
completed | May 22, 2026, 10:50 a.m. |
Created at: April 18, 2026, 6:05 a.m.