Triple
T26773580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William O’Leary |
E670052
|
entity |
| Predicate | portrayed |
P1668
|
FINISHED |
| Object |
Marty Taylor
Marty Taylor is a recurring character on the 1990s sitcom "Home Improvement," known as Tim Taylor’s younger brother who often appears in family-centered storylines.
|
E1741160
|
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: Marty Taylor | Statement: [William O’Leary, portrayed, Marty Taylor]
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: Marty Taylor Triple: [William O’Leary, portrayed, Marty Taylor]
Generated description
Marty Taylor is a recurring character on the 1990s sitcom "Home Improvement," known as Tim Taylor’s younger brother who often appears in family-centered storylines.
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_69eeb31c925881909b597f6e40056d28 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f61930bca08190a3eb5073627bacce |
completed | May 2, 2026, 3:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1209581948819096c70dcfc1d51245 |
completed | May 23, 2026, 8:08 p.m. |
| NEDg | Description generation | batch_6a120a567424819083cc2364aa6ec9da |
completed | May 23, 2026, 8:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a120b5561cc81909195b6d74ec74b50 |
completed | May 23, 2026, 8:17 p.m. |
Created at: April 27, 2026, 4:03 a.m.