Triple
T10629042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuanku Muhriz |
E250402
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Seremban |
E276229
|
NE FINISHED |
How this triple was built (2 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: Seremban | Statement: [Tuanku Muhriz, birthPlace, Seremban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seremban Context triple: [Tuanku Muhriz, birthPlace, Seremban]
-
A.
Seremban
chosen
Seremban is the capital city of the Malaysian state of Negeri Sembilan, known as an administrative, commercial, and cultural center in the western part of Peninsular Malaysia.
-
B.
Batu Pahat
Batu Pahat is a coastal town and important commercial and industrial hub in the Malaysian state of Johor.
-
C.
Kuala Pilah
Kuala Pilah is a historic inland town in the Malaysian state of Negeri Sembilan, known for its traditional Minangkabau cultural heritage and role as an administrative and commercial center for the surrounding rural district.
-
D.
Kuala Kangsar
Kuala Kangsar is a historic royal town in the Malaysian state of Perak, known as the traditional seat of the Perak Sultanate.
-
E.
Temerloh
Temerloh is a town in central Pahang, Malaysia, known as a regional commercial hub and gateway to the state's interior.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df92f8388190a8bcff96809d8eb4 |
completed | April 8, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de552d2d548190b6ade494ef2cbe7e |
completed | April 14, 2026, 2:54 p.m. |
Created at: April 8, 2026, 8:59 p.m.