Triple
T10286081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howard Hickman |
E241229
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object |
Hickman
Hickman is an English-origin surname borne by various notable individuals across fields such as politics, sports, and the arts.
|
E852712
|
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: Hickman | Statement: [Howard Hickman, hasFamilyName, Hickman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hickman Context triple: [Howard Hickman, hasFamilyName, Hickman]
-
A.
Erdman
Erdman is a masculine given name most notably borne by Disney story artist and screenwriter Erdman Penner.
-
B.
Hooperman
Hooperman is an American television dramedy series from the late 1980s starring John Ritter as a San Francisco police inspector balancing his personal and professional life.
-
C.
Hartman
Hartman is a surname of Germanic origin borne by various notable individuals across fields such as entertainment, academia, and politics.
-
D.
Pittman
Pittman is a surname of English origin borne by various notable individuals across politics, sports, and the arts.
-
E.
Darrow
Darrow is a surname most famously associated with Clarence Darrow, the prominent American lawyer and civil libertarian known for high-profile cases in the early 20th century.
- 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: Hickman Triple: [Howard Hickman, hasFamilyName, Hickman]
Generated description
Hickman is an English-origin surname borne by various notable individuals across fields such as politics, sports, and the arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hickman Target entity description: Hickman is an English-origin surname borne by various notable individuals across fields such as politics, sports, and the arts.
-
A.
Erdman
Erdman is a masculine given name most notably borne by Disney story artist and screenwriter Erdman Penner.
-
B.
Hooperman
Hooperman is an American television dramedy series from the late 1980s starring John Ritter as a San Francisco police inspector balancing his personal and professional life.
-
C.
Hartman
Hartman is a surname of Germanic origin borne by various notable individuals across fields such as entertainment, academia, and politics.
-
D.
Pittman
Pittman is a surname of English origin borne by various notable individuals across politics, sports, and the arts.
-
E.
Darrow
Darrow is a surname most famously associated with Clarence Darrow, the prominent American lawyer and civil libertarian known for high-profile cases in the early 20th century.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2b8343c819087c50e5471c46e3f |
completed | April 7, 2026, 9:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f84d432c8190a7d33e6c9f8ba8f2 |
completed | April 9, 2026, 12:52 a.m. |
| NEDg | Description generation | batch_69d6fcae243c819095a2e791716805bd |
completed | April 9, 2026, 1:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6fd3495fc8190a093d2536cfbe58a |
completed | April 9, 2026, 1:13 a.m. |
Created at: April 6, 2026, 11:40 a.m.