Triple
T10925001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TS |
E258041
|
entity |
| Predicate | firstTwoLettersIndicate |
P27166
|
FINISHED |
| Object | state or union territory |
—
|
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: state or union territory | Statement: [TS, firstTwoLettersIndicate, state or union territory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstTwoLettersIndicate Context triple: [TS, firstTwoLettersIndicate, state or union territory]
-
A.
secondLetterRepresents
Indicates that the second letter of one entity stands for, symbolizes, or denotes another entity or concept.
-
B.
firstLetter
Indicates that one entity is the initial character or starting letter of another entity (typically a string or word).
-
C.
hasInitialLetters
chosen
Indicates that one entity’s initial letters or acronym are derived from or correspond to the other entity.
-
D.
secondLetterMatches
Indicates that the second character of one string or sequence is the same as the second character of another string or sequence.
-
E.
firstCharactersOfPlate
Indicates that one entity specifies the initial characters appearing at the beginning of another entity’s license plate identifier.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708f7ab48190b60a4bb8fdb17c8e |
completed | April 9, 2026, 9:25 a.m. |
| PD | Predicate disambiguation | batch_69d72e799f808190b6ab64fc7586a303 |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:22 p.m.