Triple
T6780475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanah Kusir Cemetery |
E155667
|
entity |
| Predicate | hasNotableBurial |
P196
|
FINISHED |
| Object |
Didi Petet
Didi Petet was a prominent Indonesian actor and comedian best known for his roles in popular films and television series from the 1980s and 1990s.
|
E618438
|
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: Didi Petet | Statement: [Tanah Kusir Cemetery, hasNotableBurial, Didi Petet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Didi Petet Context triple: [Tanah Kusir Cemetery, hasNotableBurial, Didi Petet]
-
A.
Didi
Didi was a legendary Brazilian attacking midfielder, renowned for his playmaking brilliance and key role in Brazil’s World Cup victories in 1958 and 1962.
-
B.
Didi Dache
Didi Dache was a pioneering Chinese taxi-hailing mobile app that later became part of the ride-hailing giant Didi Chuxing.
-
C.
Pepa
Pepa is a traditional Assamese wind instrument, typically made from buffalo horn, used in folk and Bihu music.
-
D.
Pepa
Pepa is the stage name of Sandra Denton, a rapper best known as one-third of the pioneering hip hop group Salt-N-Pepa.
-
E.
Vavá
Vavá was a prolific Brazilian striker renowned for scoring in two consecutive World Cup finals and helping Brazil win the 1958 and 1962 tournaments.
- 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: Didi Petet Triple: [Tanah Kusir Cemetery, hasNotableBurial, Didi Petet]
Generated description
Didi Petet was a prominent Indonesian actor and comedian best known for his roles in popular films and television series from the 1980s and 1990s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Didi Petet Target entity description: Didi Petet was a prominent Indonesian actor and comedian best known for his roles in popular films and television series from the 1980s and 1990s.
-
A.
Didi
Didi was a legendary Brazilian attacking midfielder, renowned for his playmaking brilliance and key role in Brazil’s World Cup victories in 1958 and 1962.
-
B.
Didi Dache
Didi Dache was a pioneering Chinese taxi-hailing mobile app that later became part of the ride-hailing giant Didi Chuxing.
-
C.
Pepa
Pepa is a traditional Assamese wind instrument, typically made from buffalo horn, used in folk and Bihu music.
-
D.
Pepa
Pepa is the stage name of Sandra Denton, a rapper best known as one-third of the pioneering hip hop group Salt-N-Pepa.
-
E.
Vavá
Vavá was a prolific Brazilian striker renowned for scoring in two consecutive World Cup finals and helping Brazil win the 1958 and 1962 tournaments.
- 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_69c688162bf8819088b664b5c3b5be7a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d26b32c0819093f86b1002260660 |
completed | March 27, 2026, 6:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c712d27a388190ab44e6e754019fca |
completed | March 27, 2026, 11:29 p.m. |
| NEDg | Description generation | batch_69c714d7fad881909efac3b422d89d58 |
completed | March 27, 2026, 11:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c71532a18881909b2cb43c17652f57 |
completed | March 27, 2026, 11:39 p.m. |
Created at: March 27, 2026, 2:14 p.m.