Triple
T33493897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yong Siew Toh |
E857812
|
entity |
| Predicate | hasConservatoryNamedAfter |
P98126
|
FINISHED |
| Object | Yong Siew Toh Conservatory of Music |
E243853
|
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: Yong Siew Toh Conservatory of Music | Statement: [Yong Siew Toh, hasConservatoryNamedAfter, Yong Siew Toh Conservatory of Music]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasConservatoryNamedAfter Context triple: [Yong Siew Toh, hasConservatoryNamedAfter, Yong Siew Toh Conservatory of Music]
-
A.
hasConservatoryOrigin
Indicates that something originates from, or is derived from, a conservatory (such as a music or arts conservatory).
-
B.
hasConservatoryType
Indicates the specific type or classification of a conservatory associated with an entity.
-
C.
hasAcademicChairNamedAfter
Indicates that an academic chair or professorship is named in honor of a particular person or entity.
-
D.
hasAcademicDivisionNamedAfter
chosen
Indicates that an academic division (such as a school, college, or department) is named in honor of a particular person or entity.
-
E.
hasNotableInstrumentNamedAfter
Indicates that an entity has a notable musical instrument that is named after it.
- F. None of above.
Provenance (4 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_69f3497660508190a541826a81f7e9ab |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3595bd71b88190b2936f58cd0cab5e |
completed | June 19, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a0379f505c88190ac0879ab422c3054 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:38 a.m.