Triple
T14986353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LW |
E373711
|
entity |
| Predicate | isAbbreviationFor |
P43
|
FINISHED |
| Object |
Lawrence West
Lawrence West is a subway station on Line 1 Yonge–University of the Toronto Transit Commission in Toronto, Canada.
|
E1134369
|
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: Lawrence West | Statement: [LW, isAbbreviationFor, Lawrence West]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lawrence West Context triple: [LW, isAbbreviationFor, Lawrence West]
-
A.
Lawrence Treat
Lawrence Treat was an American mystery writer and pioneer of the modern police procedural, best known for his influential crime novels and short stories.
-
B.
Lawrence Jordan
Lawrence Jordan is a film editor known for his work on various feature films, including mainstream action and family movies.
-
C.
Lawrence Guth
Lawrence Guth is an American mathematician known for his influential work in harmonic analysis, combinatorial geometry, and incidence geometry.
-
D.
Lawrence Fassett
Lawrence Fassett is a manipulative CIA operative who orchestrates a deadly surveillance scheme in Robert Ludlum’s thriller "The Osterman Weekend."
-
E.
Lawrence Mark
Lawrence Mark is an American film and television producer known for acclaimed movies such as "Jerry Maguire," "As Good as It Gets," and "Dreamgirls."
- 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: Lawrence West Triple: [LW, isAbbreviationFor, Lawrence West]
Generated description
Lawrence West is a subway station on Line 1 Yonge–University of the Toronto Transit Commission in Toronto, Canada.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lawrence West Target entity description: Lawrence West is a subway station on Line 1 Yonge–University of the Toronto Transit Commission in Toronto, Canada.
-
A.
Lawrence Treat
Lawrence Treat was an American mystery writer and pioneer of the modern police procedural, best known for his influential crime novels and short stories.
-
B.
Lawrence Jordan
Lawrence Jordan is a film editor known for his work on various feature films, including mainstream action and family movies.
-
C.
Lawrence Guth
Lawrence Guth is an American mathematician known for his influential work in harmonic analysis, combinatorial geometry, and incidence geometry.
-
D.
Lawrence Fassett
Lawrence Fassett is a manipulative CIA operative who orchestrates a deadly surveillance scheme in Robert Ludlum’s thriller "The Osterman Weekend."
-
E.
Lawrence Mark
Lawrence Mark is an American film and television producer known for acclaimed movies such as "Jerry Maguire," "As Good as It Gets," and "Dreamgirls."
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7007588819095bb1de029a6f2eb |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dc625888190bf98eecf5f5b6707 |
completed | May 9, 2026, 2:36 a.m. |
| NEDg | Description generation | batch_69fea1f203a48190a9cd007727ffb5be |
completed | May 9, 2026, 2:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fea27494ec8190ac6b796b12655624 |
completed | May 9, 2026, 2:56 a.m. |
Created at: April 10, 2026, 2:52 a.m.