Triple
T16242329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sherm Lollar |
E394280
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Sherman
Sherman is a masculine given name of English origin that has been borne by various notable figures in American history and culture.
|
E1201951
|
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: Sherman | Statement: [Sherm Lollar, givenName, Sherman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sherman Context triple: [Sherm Lollar, givenName, Sherman]
-
A.
Sherman
Sherman is a city in north-central Texas that serves as a regional hub for commerce and transportation in the Texoma area.
-
B.
Sherman
Sherman is the bumbling yet kind-hearted scientist protagonist portrayed by Eddie Murphy in the comedy film "The Nutty Professor."
-
C.
Sherman
Sherman is a surname of English origin borne by numerous notable individuals across politics, military history, and the arts.
-
D.
Sherman
Sherman is the given name of American actor Sherman Hemsley, best known for portraying George Jefferson on the television sitcoms "All in the Family" and "The Jeffersons."
-
E.
Sherman
Sherman is the curious and good-hearted young boy who travels through time with his genius dog guardian in the animated franchise "Mr. Peabody & Sherman."
- 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: Sherman Triple: [Sherm Lollar, givenName, Sherman]
Generated description
Sherman is a masculine given name of English origin that has been borne by various notable figures in American history and culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sherman Target entity description: Sherman is a masculine given name of English origin that has been borne by various notable figures in American history and culture.
-
A.
Sherman
Sherman is a surname of English origin borne by numerous notable individuals across politics, military history, and the arts.
-
B.
Sherman
Sherman is the given name of American actor Sherman Hemsley, best known for portraying George Jefferson on the television sitcoms "All in the Family" and "The Jeffersons."
-
C.
Sherman
Sherman is a city in north-central Texas that serves as a regional hub for commerce and transportation in the Texoma area.
-
D.
Sherman
Sherman is the bumbling yet kind-hearted scientist protagonist portrayed by Eddie Murphy in the comedy film "The Nutty Professor."
-
E.
Sherman
Sherman is the curious and good-hearted young boy who travels through time with his genius dog guardian in the animated franchise "Mr. Peabody & Sherman."
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2455eeb4c81909066a8af78329ef3 |
completed | April 17, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000edf64a88190a9dd0c591c742977 |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a00108174ac8190b3c421b115b7190e |
completed | May 10, 2026, 4:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0010f40d6081909927e8281ab17580 |
completed | May 10, 2026, 5 a.m. |
Created at: April 10, 2026, 5:04 a.m.