Triple
T8448461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cesar Montano |
E199739
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Muro Ami
Muro Ami is a Filipino drama film that powerfully depicts the harsh realities of child labor in the destructive muro-ami fishing industry.
|
E733621
|
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: Muro Ami | Statement: [Cesar Montano, notableWork, Muro Ami]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Muro Ami Context triple: [Cesar Montano, notableWork, Muro Ami]
-
A.
Miho no Matsubara
Miho no Matsubara is a scenic coastal pine grove and beach in Shizuoka, Japan, famed for its views of Mount Fuji and its appearance in traditional art and folklore.
-
B.
Munefusa
Munefusa is the birth name of Matsuo Bashō, the renowned 17th-century Japanese haiku poet and travel writer.
-
C.
Komuro
Komuro is the married surname of Japan’s former Princess Mako, adopted after her marriage to commoner Kei Komuro.
-
D.
Amuesha
Amuesha is another name for the Yaneshaʼ language, an Arawakan language spoken by the Yaneshaʼ (Amuesha) people of central Peru.
-
E.
Miyabi
Miyabi is a traditional Japanese-inspired lighting theme used on Tokyo Skytree, characterized by elegant, refined color schemes that evoke classical aesthetics.
- 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: Muro Ami Triple: [Cesar Montano, notableWork, Muro Ami]
Generated description
Muro Ami is a Filipino drama film that powerfully depicts the harsh realities of child labor in the destructive muro-ami fishing industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Muro Ami Target entity description: Muro Ami is a Filipino drama film that powerfully depicts the harsh realities of child labor in the destructive muro-ami fishing industry.
-
A.
Miho no Matsubara
Miho no Matsubara is a scenic coastal pine grove and beach in Shizuoka, Japan, famed for its views of Mount Fuji and its appearance in traditional art and folklore.
-
B.
Munefusa
Munefusa is the birth name of Matsuo Bashō, the renowned 17th-century Japanese haiku poet and travel writer.
-
C.
Komuro
Komuro is the married surname of Japan’s former Princess Mako, adopted after her marriage to commoner Kei Komuro.
-
D.
Amuesha
Amuesha is another name for the Yaneshaʼ language, an Arawakan language spoken by the Yaneshaʼ (Amuesha) people of central Peru.
-
E.
Miyabi
Miyabi is a traditional Japanese-inspired lighting theme used on Tokyo Skytree, characterized by elegant, refined color schemes that evoke classical aesthetics.
- 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_69ca83170f9081909cd98f55614c6476 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe445b7988190b53ae45070c70d1d |
completed | March 31, 2026, 3:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce1dbf2e2c8190b20e842438acb4d5 |
completed | April 2, 2026, 7:41 a.m. |
| NEDg | Description generation | batch_69ce1eddb9988190a77ab59f2867ad5d |
completed | April 2, 2026, 7:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce1fb498448190a2737b8895f6bb48 |
completed | April 2, 2026, 7:50 a.m. |
Created at: March 30, 2026, 6:09 p.m.