Triple
T22710174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LispWorks |
E561570
|
entity |
| Predicate | previousDeveloper |
P97
|
FINISHED |
| Object |
Harlequin
Harlequin was a software company best known for developing LispWorks and other advanced programming language tools in the 1980s and 1990s.
|
E1550241
|
NE FINISHED |
How this triple was built (4 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: Harlequin | Statement: [LispWorks, previousDeveloper, Harlequin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harlequin Context triple: [LispWorks, previousDeveloper, Harlequin]
-
A.
Harlequin
Harlequin is a classic comic servant character from the Italian commedia dell’arte tradition, known for his colorful diamond-patterned costume, acrobatic antics, and mischievous, witty personality.
-
B.
Harlequin
Harlequin is a major publishing imprint best known for its extensive catalog of romance novels and commercial fiction.
-
C.
Harlequin Enterprises
Harlequin Enterprises is a major publisher best known for its mass-market romance novels and global reach in the popular fiction market.
-
D.
Mills & Boon
Mills & Boon is a British publishing house best known for its prolific output of popular romance novels.
-
E.
Harlequin Valentine
Harlequin Valentine is a dark, modern retelling of the Harlequin and Columbine commedia dell’arte myth written by Neil Gaiman.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Harlequin Triple: [LispWorks, previousDeveloper, Harlequin]
Generated description
Harlequin was a software company best known for developing LispWorks and other advanced programming language tools in the 1980s and 1990s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Harlequin Target entity description: Harlequin was a software company best known for developing LispWorks and other advanced programming language tools in the 1980s and 1990s.
-
A.
Harlequin
Harlequin is a classic comic servant character from the Italian commedia dell’arte tradition, known for his colorful diamond-patterned costume, acrobatic antics, and mischievous, witty personality.
-
B.
Harlequin
Harlequin is a major publishing imprint best known for its extensive catalog of romance novels and commercial fiction.
-
C.
Harlequin Enterprises
Harlequin Enterprises is a major publisher best known for its mass-market romance novels and global reach in the popular fiction market.
-
D.
Mills & Boon
Mills & Boon is a British publishing house best known for its prolific output of popular romance novels.
-
E.
Harlequin Valentine
Harlequin Valentine is a dark, modern retelling of the Harlequin and Columbine commedia dell’arte myth written by Neil Gaiman.
- F. None of above. chosen
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_69e2454f1348819088d83f420925a5c1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178d1e24881909ebd4531c0daef7f |
completed | April 29, 2026, 3:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b7ed9cb908190b50dd5297ca2d17c |
completed | May 18, 2026, 9:04 p.m. |
| NEDg | Description generation | batch_6a0b7fbb53388190b0942c28b5748c60 |
completed | May 18, 2026, 9:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b806b4e1481909babc97a8914947a |
completed | May 18, 2026, 9:11 p.m. |
Created at: April 17, 2026, 3:17 p.m.