Triple
T3777077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PAW Patrol: The Movie |
E83332
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Marshall
Marshall is the enthusiastic Dalmatian fire-pup and medic from the PAW Patrol franchise, known for his clumsiness, big heart, and heroic rescues.
|
E387367
|
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: Marshall | Statement: [PAW Patrol: The Movie, mainCharacter, Marshall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marshall Context triple: [PAW Patrol: The Movie, mainCharacter, Marshall]
-
A.
Marshall
Marshall is a common English surname borne by numerous notable figures, including military leaders, politicians, and artists.
-
B.
Warren
Warren is a common English surname borne by numerous notable figures in politics, law, entertainment, and other fields.
-
C.
Warren
Warren is a rural town in the Orana region of New South Wales, Australia, known for its agriculture and proximity to the Macquarie River.
-
D.
Warren
Warren is the given name of Warren Buffett, the renowned American investor and longtime CEO of Berkshire Hathaway.
-
E.
Warren
Warren is a large suburban city in southeast Michigan known for its extensive automotive and defense manufacturing industries.
- 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: Marshall Triple: [PAW Patrol: The Movie, mainCharacter, Marshall]
Generated description
Marshall is the enthusiastic Dalmatian fire-pup and medic from the PAW Patrol franchise, known for his clumsiness, big heart, and heroic rescues.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marshall Target entity description: Marshall is the enthusiastic Dalmatian fire-pup and medic from the PAW Patrol franchise, known for his clumsiness, big heart, and heroic rescues.
-
A.
Marshall
Marshall is a common English surname borne by numerous notable figures, including military leaders, politicians, and artists.
-
B.
Warren
Warren is the given name of Warren Buffett, the renowned American investor and longtime CEO of Berkshire Hathaway.
-
C.
Warren
Warren is a common English surname borne by numerous notable figures in politics, law, entertainment, and other fields.
-
D.
Warren
Warren is a rural town in the Orana region of New South Wales, Australia, known for its agriculture and proximity to the Macquarie River.
-
E.
Warren
Warren is a large suburban city in southeast Michigan known for its extensive automotive and defense manufacturing industries.
- 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_69ad8b235e608190b5a2b1d1bfcef50b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc5d3dbc8190b6ab118a56acd5a3 |
completed | March 8, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e534a02c8190b8dd76ed965f393f |
completed | March 14, 2026, 4:33 a.m. |
| NEDg | Description generation | batch_69b4e6b1cefc8190971e9441dc145e19 |
completed | March 14, 2026, 4:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4ea8b9d2c819088db1fdf9dc90c0c |
completed | March 14, 2026, 4:56 a.m. |
Created at: March 8, 2026, 3:36 p.m.