Triple
T3411391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PiTaPa |
E71901
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object |
manaca
manaca is a rechargeable contactless smart card used for public transportation and electronic payments in the Nagoya area of Japan.
|
E355486
|
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: manaca | Statement: [PiTaPa, compatibleWith, manaca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: manaca Context triple: [PiTaPa, compatibleWith, manaca]
-
A.
MaNa
MaNa is a professional StarCraft II player known for competing at the highest levels of international esports tournaments.
-
B.
Nakanamanga
Nakanamanga is an Oceanic Austronesian language spoken primarily on Efate Island and nearby areas in Vanuatu.
-
C.
Manziana
Manziana is a small town in the Lazio region of central Italy, northwest of Rome, known for its wooded nature reserve and volcanic landscape.
-
D.
Mineta
Mineta is a Japanese surname most prominently associated with Norman Mineta, a longtime U.S. politician and former Secretary of Transportation.
-
E.
Manjaco
The Manjaco are an ethnic group of West Africa, primarily living in Guinea-Bissau, known for their rice cultivation, coastal settlements, and distinct language and cultural traditions.
- 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: manaca Triple: [PiTaPa, compatibleWith, manaca]
Generated description
manaca is a rechargeable contactless smart card used for public transportation and electronic payments in the Nagoya area of Japan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: manaca Target entity description: manaca is a rechargeable contactless smart card used for public transportation and electronic payments in the Nagoya area of Japan.
-
A.
MaNa
MaNa is a professional StarCraft II player known for competing at the highest levels of international esports tournaments.
-
B.
Nakanamanga
Nakanamanga is an Oceanic Austronesian language spoken primarily on Efate Island and nearby areas in Vanuatu.
-
C.
Manziana
Manziana is a small town in the Lazio region of central Italy, northwest of Rome, known for its wooded nature reserve and volcanic landscape.
-
D.
Mineta
Mineta is a Japanese surname most prominently associated with Norman Mineta, a longtime U.S. politician and former Secretary of Transportation.
-
E.
Manjaco
The Manjaco are an ethnic group of West Africa, primarily living in Guinea-Bissau, known for their rice cultivation, coastal settlements, and distinct language and cultural traditions.
- 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_69ad85ac312481909e7027ced1456a9f |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb90a76288190b92ef3b26638cd47 |
completed | March 8, 2026, 5:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b34bdf81e48190abac8ea645e929ce |
completed | March 12, 2026, 11:27 p.m. |
| NEDg | Description generation | batch_69b34e4972008190af3b84f26b4a3629 |
completed | March 12, 2026, 11:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b34fc6c3f88190ba1a08243232df05 |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 8, 2026, 3:15 p.m.