Triple
T15355589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lego Ninjago |
E367163
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Jay
Jay is one of the central ninja heroes in the Lego Ninjago franchise, known for his mastery of lightning and his energetic, humorous personality.
|
E1151114
|
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: Jay | Statement: [Lego Ninjago, hasMainCharacter, Jay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jay Context triple: [Lego Ninjago, hasMainCharacter, Jay]
-
A.
Jay
Jay is the surname of John Jay, a prominent American Founding Father and the first Chief Justice of the United States.
-
B.
Jay
Jay is a small town located in Santa Rosa County in the northwestern part of Florida.
-
C.
Jay
Jay is a former American football placekicker and current sports commentator best known for his long NFL career and broadcasting work.
-
D.
Jay
Jay is a foul-mouthed, slacker stoner character from Kevin Smith’s View Askewniverse, best known as one half of the comedic duo Jay and Silent Bob.
-
E.
Jay
Jay is the given name of Whittaker Chambers, the American writer and former Soviet spy best known for his role in the Alger Hiss case.
- 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: Jay Triple: [Lego Ninjago, hasMainCharacter, Jay]
Generated description
Jay is one of the central ninja heroes in the Lego Ninjago franchise, known for his mastery of lightning and his energetic, humorous personality.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jay Target entity description: Jay is one of the central ninja heroes in the Lego Ninjago franchise, known for his mastery of lightning and his energetic, humorous personality.
-
A.
Jay
Jay is a key young activist character in the film "Okja," involved in the animal-rights resistance against a powerful corporation.
-
B.
Jay
Jay is a foul-mouthed, slacker stoner character from Kevin Smith’s View Askewniverse, best known as one half of the comedic duo Jay and Silent Bob.
-
C.
Jay
Jay is a masculine given name commonly used in English-speaking countries, often derived from the jaybird or as a short form of names like Jason or James.
-
D.
Jay
Jay is a former American football placekicker and current sports commentator best known for his long NFL career and broadcasting work.
-
E.
Jay
Jay is the given name of Whittaker Chambers, the American writer and former Soviet spy best known for his role in the Alger Hiss case.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e2c00648190ae2325e1ee58dcfd |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff02012fa48190a108f1ca710ffb15 |
completed | May 9, 2026, 9:44 a.m. |
| NEDg | Description generation | batch_69ff02d0d0188190b6414a52bf8e6e54 |
completed | May 9, 2026, 9:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff037ddf1c8190b7c93c1b3edf33e8 |
completed | May 9, 2026, 9:50 a.m. |
Created at: April 10, 2026, 3:18 a.m.