Triple
T12006944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LLE |
E285803
|
entity |
| Predicate | hasAlphabeticCode |
P50778
|
FINISHED |
| Object | LLE |
—
|
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: LLE | Statement: [LLE, hasAlphabeticCode, LLE]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlphabeticCode Context triple: [LLE, hasAlphabeticCode, LLE]
-
A.
alphabeticCode
Indicates that one entity is identified or represented by a specific alphabetic code assigned to it.
-
B.
hasFourLetterCode
Indicates that an entity is associated with a code consisting of exactly four characters.
-
C.
hasCodeLetters
chosen
Indicates that an entity is associated with, or represented by, a specific sequence of letters used as its code or identifier.
-
D.
hasISOCode
Indicates that an entity is associated with a specific standardized ISO code that uniquely identifies it according to ISO conventions.
-
E.
hasLinguisticCode
Indicates that an entity is associated with a specific linguistic identifier or code (such as a language or script code) that characterizes its linguistic properties.
- 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c5cfc08190821e4b2940c51416 |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902b245cc8190af96a9c2bd9c6250 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.