Triple
T35220924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lola Petticrew |
E1016950
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
A Bump Along the Way
A Bump Along the Way is a 2019 Irish comedy-drama film about a middle-aged single mother in Derry who unexpectedly becomes pregnant, forcing her and her teenage daughter to reassess their relationship and lives.
|
E2130587
|
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: A Bump Along the Way | Statement: [Lola Petticrew, notableWork, A Bump Along the Way]
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: A Bump Along the Way Triple: [Lola Petticrew, notableWork, A Bump Along the Way]
Generated description
A Bump Along the Way is a 2019 Irish comedy-drama film about a middle-aged single mother in Derry who unexpectedly becomes pregnant, forcing her and her teenage daughter to reassess their relationship and lives.
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_69f76de072908190ab65038a8a7b6a79 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78ea267888190b4b15717f01c5b54 |
completed | May 3, 2026, 6:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a380410c2e88190b0fe80078f582794 |
completed | June 21, 2026, 3:32 p.m. |
| NEDg | Description generation | batch_6a3804c6a8788190ac07c698d78a290d |
completed | June 21, 2026, 3:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a38057811848190a12d3e760db65b2d |
completed | June 21, 2026, 3:38 p.m. |
Created at: May 3, 2026, 4:02 p.m.