Triple
T21410470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinatown (Washington, D.C.) |
E528153
|
entity |
| Predicate | hasStreetSignage |
P5950
|
FINISHED |
| Object | bilingual English-Chinese signs |
—
|
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: bilingual English-Chinese signs | Statement: [Chinatown (Washington, D.C.), hasStreetSignage, bilingual English-Chinese signs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStreetSignage Context triple: [Chinatown (Washington, D.C.), hasStreetSignage, bilingual English-Chinese signs]
-
A.
hasRoadSign
Indicates that one entity possesses, displays, or is associated with a particular road sign.
-
B.
hasSignageIn
Indicates that appropriate signs or signage for an entity are present or installed within a specified location or area.
-
C.
hasSignage
chosen
Indicates that appropriate signs or visual markers are present to convey information, directions, warnings, or identification related to the associated entity.
-
D.
hasSignageName
Indicates that an entity has a specific name or label as it appears on its physical signage.
-
E.
hasTouristSignage
Indicates that there is official signage present to guide or inform tourists about a place, route, or attraction.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b1b6595481908cdf915e3585ed08 |
completed | April 22, 2026, 11:32 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:42 p.m.