Triple
T9049982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pham Ngu Lao backpacker area |
E216856
|
entity |
| Predicate | commonForeignLanguage |
P1253
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Pham Ngu Lao backpacker area, commonForeignLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonForeignLanguage Context triple: [Pham Ngu Lao backpacker area, commonForeignLanguage, English]
-
A.
otherLanguage
Indicates that an entity has or uses an additional language distinct from its primary or main language.
-
B.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
C.
taughtAsForeignLanguageIn
chosen
Indicates that a language is taught as a foreign (non-native) language within a particular educational context or institution.
-
D.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
-
E.
languagesSpoken
Indicates that an entity is able to communicate using one or more specified languages.
- 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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b52cc1881909fb011d9a8af2e18 |
completed | April 1, 2026, 12:48 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee566b081909e3cdaf551dbd0ec |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:10 p.m.