Triple
T32840502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lamington Presbyterian Church Cemetery |
E839951
|
entity |
| Predicate | hasPrimaryLanguageOfInscriptions |
P15804
|
FINISHED |
| Object | English |
—
|
LITERAL 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: English | Statement: [Lamington Presbyterian Church Cemetery, hasPrimaryLanguageOfInscriptions, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryLanguageOfInscriptions Context triple: [Lamington Presbyterian Church Cemetery, hasPrimaryLanguageOfInscriptions, English]
-
A.
secondaryLanguageOfInscriptions
Indicates that a specified language serves as the secondary language used in the inscriptions associated with a given entity.
-
B.
inscriptionsLanguage
chosen
Indicates that the language used in the inscriptions on an object or surface is the specified language.
-
C.
officialLanguageOfInscriptions
Indicates the language officially used in the inscriptions associated with a particular entity.
-
D.
hasTypeOfInscriptions
Indicates that an entity bears or is associated with a specific kind or category of inscriptions.
-
E.
hasArabicInscription
Indicates that an entity bears or contains an inscription written in the Arabic script or language.
- F. None of above.
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_69f3493ff0888190b51e974eae2a7834 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a0123d1162c81908182d01ba3ddd236 |
completed | May 11, 2026, 12:33 a.m. |
| PD | Predicate disambiguation | batch_6a01236713d88190b12a567c0dfb2d49 |
completed | May 11, 2026, 12:31 a.m. |
Created at: May 1, 2026, 1:16 a.m.