Triple
T23911251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marjoe Gortner |
E601943
|
entity |
| Predicate | actedIn |
P1668
|
FINISHED |
| Object |
When You Comin' Back, Red Ryder?
"When You Comin' Back, Red Ryder?" is a tense 1979 drama film adaptation of Mark Medoff's play, centered on a violent drifter who terrorizes the patrons of a small-town diner.
|
E1610857
|
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: When You Comin' Back, Red Ryder? | Statement: [Marjoe Gortner, actedIn, When You Comin' Back, Red Ryder?]
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: When You Comin' Back, Red Ryder? Triple: [Marjoe Gortner, actedIn, When You Comin' Back, Red Ryder?]
Generated description
"When You Comin' Back, Red Ryder?" is a tense 1979 drama film adaptation of Mark Medoff's play, centered on a violent drifter who terrorizes the patrons of a small-town diner.
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_69e2953a187081908346a9f36e85fc98 |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1ce94f65c8190807723344fa0b837 |
completed | April 29, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f7635085c81908522e05182cacf75 |
completed | May 21, 2026, 9:16 p.m. |
| NEDg | Description generation | batch_6a0f76f2b5248190b92095f8003001be |
completed | May 21, 2026, 9:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f78df8c9c81908eb3912b212862f9 |
completed | May 21, 2026, 9:27 p.m. |
Created at: April 17, 2026, 8:38 p.m.