Triple
T12620116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Type 3 Indian Princess gold dollar |
E301355
|
entity |
| Predicate | obverseInscriptionLanguage |
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: [Type 3 Indian Princess gold dollar, obverseInscriptionLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: obverseInscriptionLanguage Context triple: [Type 3 Indian Princess gold dollar, obverseInscriptionLanguage, English]
-
A.
obverseText
Indicates the text that appears on the front (obverse) side of an object, typically a coin or medal.
-
B.
inscriptionsLanguage
chosen
Indicates that the language used in the inscriptions on an object or surface is the specified language.
-
C.
secondaryLanguageOfInscriptions
Indicates that a specified language serves as the secondary language used in the inscriptions associated with a given entity.
-
D.
officialLanguageOfInscriptions
Indicates the language officially used in the inscriptions associated with a particular entity.
-
E.
materialTypicallyInscribedOn
Indicates the material that is most commonly used as the surface or medium on which something is inscribed.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617b07ec8190b714f04ae6654060 |
completed | April 10, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69d960b195108190ac25bd95e644ace4 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:13 p.m.