Triple
T5140509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Ross |
E115936
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object |
Joe Ross
Joe Ross is a relatively common personal name shared by multiple individuals across fields such as sports, entertainment, and academia.
|
E497897
|
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: Joe Ross | Statement: [Joe Ross, hasName, Joe Ross]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joe Ross Context triple: [Joe Ross, hasName, Joe Ross]
-
A.
Joe Ross
Joe Ross is the main protagonist of the film "Summer Stock," around whom the story’s central romantic and musical plot revolves.
-
B.
Thunderbolt Ross
Thunderbolt Ross is a high-ranking U.S. military officer in Marvel Comics, best known for obsessively hunting the Hulk and later becoming the Red Hulk.
-
C.
Finis Conner
Finis Conner is an American entrepreneur best known as a pioneering figure in the hard disk drive industry and co-founder of both Seagate Technology and Conner Peripherals.
-
D.
Theron Warth
Theron Warth was a film editor known for his work on mid-20th-century American cinema.
-
E.
Daniel P. McCoy
Daniel P. McCoy is an American politician who serves as the chief executive of Albany County, New York, overseeing county government operations and policy implementation.
- 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: Joe Ross Triple: [Joe Ross, hasName, Joe Ross]
Generated description
Joe Ross is a relatively common personal name shared by multiple individuals across fields such as sports, entertainment, and academia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Joe Ross Target entity description: Joe Ross is a relatively common personal name shared by multiple individuals across fields such as sports, entertainment, and academia.
-
A.
Joe Ross
Joe Ross is the main protagonist of the film "Summer Stock," around whom the story’s central romantic and musical plot revolves.
-
B.
Thunderbolt Ross
Thunderbolt Ross is a high-ranking U.S. military officer in Marvel Comics, best known for obsessively hunting the Hulk and later becoming the Red Hulk.
-
C.
Finis Conner
Finis Conner is an American entrepreneur best known as a pioneering figure in the hard disk drive industry and co-founder of both Seagate Technology and Conner Peripherals.
-
D.
Theron Warth
Theron Warth was a film editor known for his work on mid-20th-century American cinema.
-
E.
Daniel P. McCoy
Daniel P. McCoy is an American politician who serves as the chief executive of Albany County, New York, overseeing county government operations and policy implementation.
- 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_69bd44459a988190a772a5c2ec6a1965 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd787e5fe88190834042a73d4d9619 |
completed | March 20, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfe2a59881908c790e26a2365353 |
completed | March 21, 2026, 5:05 p.m. |
| NEDg | Description generation | batch_69bed07c9cd081908f8246e24b2f6458 |
completed | March 21, 2026, 5:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bed1013fe88190b76855a042226359 |
completed | March 21, 2026, 5:10 p.m. |
Created at: March 20, 2026, 1:43 p.m.