Triple
T11495735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Germany |
E272528
|
entity |
| Predicate | hasVisualCharacter |
P99801
|
FINISHED |
| Object | grand stone warehouses |
—
|
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: grand stone warehouses | Statement: [Little Germany, hasVisualCharacter, grand stone warehouses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVisualCharacter Context triple: [Little Germany, hasVisualCharacter, grand stone warehouses]
-
A.
hasTextualCharacter
Indicates that something possesses or exhibits the qualities of written or printed text, such as letters, symbols, or characters.
-
B.
hasLanguageCharacter
Indicates that an entity uses, contains, or is associated with a specific written or symbolic character from a language.
-
C.
hasVisualIndicator
Indicates that an entity is associated with some form of visual cue or marker that signals its status, condition, or presence.
-
D.
hasIconicCharacter
Indicates that something is associated with a character widely recognized as emblematic or highly representative of it.
-
E.
hasGlyphsFor
Indicates that one entity provides or contains the necessary glyphs or visual symbols to represent 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85de183f08190abfa36eaabc61dc6 |
completed | April 10, 2026, 2:18 a.m. |
| PD | Predicate disambiguation | batch_69d808736c5c8190899b5b3b2e797f65 |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d822ef46988190a1c360da4ee14fef |
completed | April 9, 2026, 10:06 p.m. |
Created at: April 8, 2026, 9:36 p.m.