Triple
T14585730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Musker |
E342310
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Musker
Musker is a surname most notably associated with American film director and animator John Musker, known for his work on several classic Disney animated features.
|
E1107916
|
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: Musker | Statement: [John Musker, familyName, Musker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Musker Context triple: [John Musker, familyName, Musker]
-
A.
Murchehkhort
Murchehkhort is a locality in central Iran historically notable as the site of the 1729 Battle of Murchehkhort during Nader Shah’s campaigns.
-
B.
El Muski
El Muski is a historic, densely populated commercial district in central Cairo known for its traditional markets and bustling street life.
-
C.
Mauzy
Mauzy is the surname of American actress Mackenzie Mauzy, known for her roles in television and film.
-
D.
Munnik
Munnik is a given name associated with J. B. M. Hertzog, a prominent early 20th-century South African prime minister and political leader.
-
E.
Muskiz
Muskiz is a coastal municipality in the province of Biscay in Spain’s Basque Country, known for its industrial facilities and nearby beaches.
- 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: Musker Triple: [John Musker, familyName, Musker]
Generated description
Musker is a surname most notably associated with American film director and animator John Musker, known for his work on several classic Disney animated features.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Musker Target entity description: Musker is a surname most notably associated with American film director and animator John Musker, known for his work on several classic Disney animated features.
-
A.
Murchehkhort
Murchehkhort is a locality in central Iran historically notable as the site of the 1729 Battle of Murchehkhort during Nader Shah’s campaigns.
-
B.
El Muski
El Muski is a historic, densely populated commercial district in central Cairo known for its traditional markets and bustling street life.
-
C.
Mauzy
Mauzy is the surname of American actress Mackenzie Mauzy, known for her roles in television and film.
-
D.
Munnik
Munnik is a given name associated with J. B. M. Hertzog, a prominent early 20th-century South African prime minister and political leader.
-
E.
Muskiz
Muskiz is a coastal municipality in the province of Biscay in Spain’s Basque Country, known for its industrial facilities and nearby beaches.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb421bb308190a457425429ef6aa5 |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94bef27481908c108110dbf21780 |
completed | May 8, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69fd95a852b88190a1daf0109ef3231e |
completed | May 8, 2026, 7:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd968b01848190b3986bde6015feb4 |
completed | May 8, 2026, 7:53 a.m. |
Created at: April 10, 2026, 1:24 a.m.