Triple
T37211268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Sturges |
E922308
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
The Old Man and the Sea
The Old Man and the Sea is a 1958 film adaptation of Ernest Hemingway’s novella, directed by John Sturges and starring Spencer Tracy as an aging Cuban fisherman.
|
E2217406
|
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: The Old Man and the Sea | Statement: [John Sturges, notableWork, The Old Man and the Sea]
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: The Old Man and the Sea Triple: [John Sturges, notableWork, The Old Man and the Sea]
Generated description
The Old Man and the Sea is a 1958 film adaptation of Ernest Hemingway’s novella, directed by John Sturges and starring Spencer Tracy as an aging Cuban fisherman.
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_69f76ea4849481909b4a3073efb0114c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb36727afc8190a5a5ef47b12f6eed |
completed | May 6, 2026, 12:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40362509988190b3c6f373ad0129cd |
completed | June 27, 2026, 8:44 p.m. |
| NEDg | Description generation | batch_6a40370e29b88190896e161008cb4262 |
completed | June 27, 2026, 8:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a4038978d88819094f50792d0db95ee |
completed | June 27, 2026, 8:54 p.m. |
Created at: May 3, 2026, 4:15 p.m.