Triple
T1437749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AS |
E30594
|
entity |
| Predicate | firstCharactersOfPlate |
P29311
|
FINISHED |
| Object | AS |
—
|
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: AS | Statement: [AS, firstCharactersOfPlate, AS]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstCharactersOfPlate Context triple: [AS, firstCharactersOfPlate, AS]
-
A.
firstLetter
Indicates that one entity is the initial character or starting letter of another entity (typically a string or word).
-
B.
hasInitialLetters
Indicates that one entity’s initial letters or acronym are derived from or correspond to the other entity.
-
C.
frontNumbering
Indicates that an entity is assigned a numbering or label that appears at the front or beginning of something (e.g., a sequence, document, or list).
-
D.
letterSequence
Indicates that one sequence of letters directly follows or is ordered in relation to another within a larger string or alphabetic arrangement.
-
E.
firstWord
Indicates that one entity is the first word in the sequence or text associated with another entity.
- 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_69a498fc69ec8190b61722bd4b67c4d2 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5ff8dbc81909eafcfc9f2260a22 |
completed | March 1, 2026, 11:04 p.m. |
| PD | Predicate disambiguation | batch_69a4c478f65481909ee716791c663491 |
completed | March 1, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69a4c5fd2c5c81909283b7a74aff89b7 |
completed | March 1, 2026, 11:04 p.m. |
Created at: March 1, 2026, 8 p.m.