Triple
T10103504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skudai |
E216259
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Senai
Senai is a town in Johor, Malaysia, best known for housing Senai International Airport and serving as a key industrial and logistics hub near Skudai.
|
E840636
|
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: Senai | Statement: [Skudai, locatedNear, Senai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Senai Context triple: [Skudai, locatedNear, Senai]
-
A.
Sene
Sene is the tenth month of the Ethiopian calendar, roughly corresponding to June in the Gregorian calendar.
-
B.
Seini
Seini is a small town in northwestern Romania, known for its industrial activities and location near the Lăpuș River in Maramureș County.
-
C.
Es Sénia
Es Sénia is a commune and suburb of Oran in northwestern Algeria, known for hosting the region’s main international airport and various industrial and educational facilities.
-
D.
Senesky
Senesky is a surname most notably associated with George Senesky, an American professional basketball player and coach in the mid-20th century.
-
E.
Senilia
Senilia is a literary work by Swedish author and academic Lars Gyllensten, reflecting his characteristic blend of philosophical reflection and experimental prose.
- 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: Senai Triple: [Skudai, locatedNear, Senai]
Generated description
Senai is a town in Johor, Malaysia, best known for housing Senai International Airport and serving as a key industrial and logistics hub near Skudai.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Senai Target entity description: Senai is a town in Johor, Malaysia, best known for housing Senai International Airport and serving as a key industrial and logistics hub near Skudai.
-
A.
Sene
Sene is the tenth month of the Ethiopian calendar, roughly corresponding to June in the Gregorian calendar.
-
B.
Seini
Seini is a small town in northwestern Romania, known for its industrial activities and location near the Lăpuș River in Maramureș County.
-
C.
Es Sénia
Es Sénia is a commune and suburb of Oran in northwestern Algeria, known for hosting the region’s main international airport and various industrial and educational facilities.
-
D.
Senesky
Senesky is a surname most notably associated with George Senesky, an American professional basketball player and coach in the mid-20th century.
-
E.
Senilia
Senilia is a literary work by Swedish author and academic Lars Gyllensten, reflecting his characteristic blend of philosophical reflection and experimental prose.
- 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_69ca83d039f08190b9d10363221c69fb |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd09af07c819099774af46ebf62d7 |
completed | April 2, 2026, 2:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b6dcca848190851f6f1968fe244c |
completed | April 5, 2026, 7:24 p.m. |
| NEDg | Description generation | batch_69d2b7e64fcc8190805a6a31c8a3786f |
completed | April 5, 2026, 7:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2b883959481909f330a26863621ae |
completed | April 5, 2026, 7:31 p.m. |
Created at: March 30, 2026, 9:03 p.m.