Triple
T2292771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tirhuta script |
E51541
|
entity |
| Predicate | hasDistinctNumerals |
P39011
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Tirhuta script, hasDistinctNumerals, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDistinctNumerals Context triple: [Tirhuta script, hasDistinctNumerals, yes]
-
A.
hasDistinctLetters
Indicates that all letters in the given string or word are unique, with no character repeated.
-
B.
hasDistinctNumeralsHistorically
Indicates that the numerals used by an entity have historically been different or have changed in form or system over time.
-
C.
hasDistinctLettersFor
Indicates that one entity is associated with another such that the letters used in the first are all different from (i.e., share no letters with) those used in the second.
-
D.
hasNumberDistinction
Indicates that a language or system grammatically distinguishes between different numbers (such as singular, plural, dual, etc.) in its expressions.
-
E.
hasDistinctCharacterSet
Indicates that two compared items use different sets of characters, with no character set being a subset or duplicate of the other.
- F. None of above. chosen
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_69a88b09c644819090b503456d96bf70 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abcd0e42248190ada33b84d75caa64 |
completed | March 7, 2026, 7 a.m. |
| PD | Predicate disambiguation | batch_69abc589295c819092989820c2b4e9d8 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abcd0d01ac8190935fe904905cb233 |
completed | March 7, 2026, 7 a.m. |
Created at: March 4, 2026, 7:48 p.m.