Triple
T23560982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grace Adler |
E579234
|
entity |
| Predicate | hasRomanticRelationshipWith |
P9994
|
FINISHED |
| Object |
Leo Markus
Leo Markus is a recurring love interest of Grace Adler on the television sitcom "Will & Grace," portrayed as a charming and successful doctor.
|
E1594309
|
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: Leo Markus | Statement: [Grace Adler, hasRomanticRelationshipWith, Leo Markus]
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: Leo Markus Triple: [Grace Adler, hasRomanticRelationshipWith, Leo Markus]
Generated description
Leo Markus is a recurring love interest of Grace Adler on the television sitcom "Will & Grace," portrayed as a charming and successful doctor.
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_69e245fe24588190888f3aec8407d8e3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1af680ee88190a23a6f9fed7ae757 |
completed | April 29, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f4560fdb0819097641dac375751fb |
completed | May 21, 2026, 5:48 p.m. |
| NEDg | Description generation | batch_6a0f4671511081908f0136d26bce0eb9 |
completed | May 21, 2026, 5:52 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f474266a08190b62dd3968b832a5a |
completed | May 21, 2026, 5:56 p.m. |
Created at: April 17, 2026, 6:16 p.m.