Triple
T13124235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MXVER |
E311802
|
entity |
| Predicate | firstTwoCharacters |
P29311
|
FINISHED |
| Object | MX |
—
|
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: MX | Statement: [MXVER, firstTwoCharacters, MX]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstTwoCharacters Context triple: [MXVER, firstTwoCharacters, MX]
-
A.
firstLetter
Indicates that one entity is the initial character or starting letter of another entity (typically a string or word).
-
B.
firstCharactersOfPlate
chosen
Indicates that one entity specifies the initial characters appearing at the beginning of another entity’s license plate identifier.
-
C.
firstCharacterType
Indicates that the type or category of the first character in a sequence or string has a specified value.
-
D.
firstWord
Indicates that one entity is the first word in the sequence or text associated with another entity.
-
E.
firstWordsOf
Indicates that one entity consists of the initial word or sequence of words taken from another entity (such as a text or utterance).
- 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_69d806a9fe888190b081e2d9ea665d6c |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9819946808190b41335fb1054accd |
completed | April 10, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69d98043a74c81908648e6cd0b4c7f71 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:07 p.m.