Triple
T22602206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Lacey |
E574856
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Matthew Lacey
Matthew Lacey is an individual known primarily as the son of John Lacey.
|
E1544476
|
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: Matthew Lacey | Statement: [John Lacey, hasChild, Matthew Lacey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Lacey Context triple: [John Lacey, hasChild, Matthew Lacey]
-
A.
Marc Lacey
Marc Lacey is an American journalist and editor best known for serving in senior leadership roles at The New York Times, including overseeing major news coverage and editorial operations.
-
B.
Jonathan Leathers
Jonathan Leathers is an American former professional soccer defender who played in Major League Soccer after a standout collegiate career at Furman University.
-
C.
Jonathan Loughran
Jonathan Loughran is a fun-loving, adventurous human character from the "Hotel Transylvania" animated film series, known for marrying Mavis Dracula and becoming part of her monster family.
-
D.
Jonathan Loughran
Jonathan Loughran is an American character actor best known for his frequent comedic roles in Adam Sandler films.
-
E.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
- 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: Matthew Lacey Triple: [John Lacey, hasChild, Matthew Lacey]
Generated description
Matthew Lacey is an individual known primarily as the son of John Lacey.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew Lacey Target entity description: Matthew Lacey is an individual known primarily as the son of John Lacey.
-
A.
Marc Lacey
Marc Lacey is an American journalist and editor best known for serving in senior leadership roles at The New York Times, including overseeing major news coverage and editorial operations.
-
B.
Jonathan Leathers
Jonathan Leathers is an American former professional soccer defender who played in Major League Soccer after a standout collegiate career at Furman University.
-
C.
Jonathan Loughran
Jonathan Loughran is an American character actor best known for his frequent comedic roles in Adam Sandler films.
-
D.
Jonathan Loughran
Jonathan Loughran is a fun-loving, adventurous human character from the "Hotel Transylvania" animated film series, known for marrying Mavis Dracula and becoming part of her monster family.
-
E.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
- 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_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1626db69481908ec9f9c7d320d3cb |
completed | April 29, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b3d682f5081909814d3fd294221d9 |
completed | May 18, 2026, 4:25 p.m. |
| NEDg | Description generation | batch_6a0b3e9fac2c819085c7b7f2ee0abe17 |
completed | May 18, 2026, 4:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b3f7afd2c81908c40d6479a826c22 |
completed | May 18, 2026, 4:34 p.m. |
Created at: April 17, 2026, 2:50 p.m.