Triple
T18498066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Laurence |
E451998
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Jean
Jean is the given first name of the Canadian novelist Margaret Laurence, a central figure in 20th-century Canadian literature.
|
E1327644
|
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: Jean | Statement: [Margaret Laurence, givenName, Jean]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Context triple: [Margaret Laurence, givenName, Jean]
-
A.
Jean
Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
-
B.
Jean
Jean is a fictional mother character from the film "Sweet Sixteen."
-
C.
Jean
Jean is the central protagonist of the crime drama film "I'm Your Woman," a young mother forced into a perilous life on the run after her husband's criminal activities unravel.
-
D.
Jean
Jean is a common French given name used for both males and females, equivalent to "John" in English.
-
E.
Jean
Jean is a central character in the Scottish musical film "Sunshine on Leith," which follows the lives and relationships of people in Edinburgh set to the music of The Proclaimers.
- 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: Jean Triple: [Margaret Laurence, givenName, Jean]
Generated description
Jean is the given first name of the Canadian novelist Margaret Laurence, a central figure in 20th-century Canadian literature.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jean Target entity description: Jean is the given first name of the Canadian novelist Margaret Laurence, a central figure in 20th-century Canadian literature.
-
A.
Jean
Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
-
B.
Jean
Jean is a common French given name used for both males and females, equivalent to "John" in English.
-
C.
Jean
Jean is the birth name of American actress, comedian, writer, and producer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
-
D.
Jean
Jean is the given first name of American film and television actress Jeff Donnell, known for her roles in mid-20th-century Hollywood productions.
-
E.
Jean
Jean is the given name of the artist who created the work titled "Allegory of Prudence."
- 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e532c275388190aafe891d0202f82e |
completed | April 19, 2026, 7:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a04713a1ec88190b80f561172adaa62 |
completed | May 13, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_6a0473c19bf881908ef53bf0447a9306 |
completed | May 13, 2026, 12:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a04743eb3388190ab92e849a95d3c97 |
completed | May 13, 2026, 12:53 p.m. |
Created at: April 10, 2026, 11:36 a.m.