Triple
T15626033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pixar theatrical short films |
E375680
|
entity |
| Predicate | notableShortFilm |
P40731
|
FINISHED |
| Object |
Lava
Lava is a Pixar animated musical short film that tells a romantic, volcano-themed love story through a Hawaiian-inspired song.
|
E1168763
|
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: Lava | Statement: [Pixar theatrical short films, notableShortFilm, Lava]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lava Context triple: [Pixar theatrical short films, notableShortFilm, Lava]
-
A.
Lava
Lava is a small hill town in West Bengal, India, known as a gateway to the Neora Valley National Park and for its cool climate and forested surroundings.
-
B.
Lava
Lava is a legendary prince in the Hindu epic Ramayana, known as one of the twin sons of Rama and Sita.
-
C.
Lava
Lava is a surname most notably borne by American film and television composer William Lava, known for his work on numerous Warner Bros. cartoons and Westerns.
-
D.
Lava
"Lava" is a nonfiction book by Andrea Warren that explores the science, danger, and human stories surrounding volcanic eruptions.
-
E.
Magma
Magma is a Marvel Comics superheroine and mutant associated with the New Mutants, known for her ability to generate and control volcanic lava and seismic energy.
- 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: Lava Triple: [Pixar theatrical short films, notableShortFilm, Lava]
Generated description
Lava is a Pixar animated musical short film that tells a romantic, volcano-themed love story through a Hawaiian-inspired song.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lava Target entity description: Lava is a Pixar animated musical short film that tells a romantic, volcano-themed love story through a Hawaiian-inspired song.
-
A.
Lava
Lava is a legendary prince in the Hindu epic Ramayana, known as one of the twin sons of Rama and Sita.
-
B.
Lava
Lava is a small hill town in West Bengal, India, known as a gateway to the Neora Valley National Park and for its cool climate and forested surroundings.
-
C.
Lava
Lava is a surname most notably borne by American film and television composer William Lava, known for his work on numerous Warner Bros. cartoons and Westerns.
-
D.
Lava
"Lava" is a nonfiction book by Andrea Warren that explores the science, danger, and human stories surrounding volcanic eruptions.
-
E.
Magma
Magma is a Marvel Comics superheroine and mutant associated with the New Mutants, known for her ability to generate and control volcanic lava and seismic energy.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9e5e248190ae54cda1fde51efb |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f415c2c81909e232e1c6531da93 |
completed | May 9, 2026, 4:22 p.m. |
| NEDg | Description generation | batch_69ff5fea7cb48190a1acb9201a12fa32 |
completed | May 9, 2026, 4:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff62e84bec81908a4885bf7f8f3749 |
completed | May 9, 2026, 4:38 p.m. |
Created at: April 10, 2026, 4:14 a.m.