Triple
T19653472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lamont |
E471874
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
LaMont
LaMont is a given name and surname, typically a variant spelling of Lamont used in English-speaking contexts.
|
E1387780
|
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: LaMont | Statement: [Lamont, hasVariant, LaMont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LaMont Context triple: [Lamont, hasVariant, LaMont]
-
A.
Leland
Leland is a masculine given name of English origin, historically associated with figures such as American industrialist and Stanford University founder Leland Stanford.
-
B.
Leland
Leland is a residential neighborhood located within the city of Compton, California.
-
C.
Boustany
Boustany is a surname of Lebanese origin notably associated with several prominent political and professional families, particularly in the United States and Lebanon.
-
D.
Tilghman
Tilghman is a surname most notably associated with Shirley M. Tilghman, a prominent molecular biologist and former president of Princeton University.
-
E.
Tilghman
Tilghman is a masculine given name of English origin that has been borne by various notable American figures, including politicians and military officers.
- 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: LaMont Triple: [Lamont, hasVariant, LaMont]
Generated description
LaMont is a given name and surname, typically a variant spelling of Lamont used in English-speaking contexts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LaMont Target entity description: LaMont is a given name and surname, typically a variant spelling of Lamont used in English-speaking contexts.
-
A.
Leland
Leland is a residential neighborhood located within the city of Compton, California.
-
B.
Leland
Leland is a masculine given name of English origin, historically associated with figures such as American industrialist and Stanford University founder Leland Stanford.
-
C.
Boustany
Boustany is a surname of Lebanese origin notably associated with several prominent political and professional families, particularly in the United States and Lebanon.
-
D.
Tilghman
Tilghman is a surname most notably associated with Shirley M. Tilghman, a prominent molecular biologist and former president of Princeton University.
-
E.
Tilghman
Tilghman is a masculine given name of English origin that has been borne by various notable American figures, including politicians and military officers.
- 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_69d8e51395348190ac1416d46dfc6db0 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e641442994819085a7d9562bf0bdcd |
completed | April 20, 2026, 3:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a077ef165b48190894f4ba843d7bcb4 |
completed | May 15, 2026, 8:15 p.m. |
| NEDg | Description generation | batch_6a078103746c8190aa687e483fef12e7 |
completed | May 15, 2026, 8:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a078216845c8190b58bfa09180aeb8f |
completed | May 15, 2026, 8:29 p.m. |
Created at: April 10, 2026, 1:44 p.m.