Triple
T25879203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Citeureup |
E651996
|
entity |
| Predicate | hasCommutersTo |
P46264
|
FINISHED |
| Object | Jakarta |
—
|
NE NERFINISHED |
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: Jakarta | Statement: [Citeureup, hasCommutersTo, Jakarta]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommutersTo Context triple: [Citeureup, hasCommutersTo, Jakarta]
-
A.
hasCommuterLinks
Indicates that there are established transportation connections enabling regular travel between two locations.
-
B.
hasCommuterTraffic
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
-
C.
hasCommuterServices
Indicates that a location or facility provides transportation services specifically intended for regular commuters, such as daily or frequent travelers between home and work or school.
-
D.
hasCommuterPopulation
chosen
Indicates that a place has a significant number of people who regularly travel to or from it for work, study, or other routine activities.
-
E.
hasCommuterPattern
Indicates that there is a characteristic or recurring pattern in how an entity regularly travels between locations, typically for work or daily activities.
- 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_69e7ab3ad9d88190841ddcb93ab02e96 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fcec5f8b448190b48330a19b462d24 |
completed | May 7, 2026, 7:47 p.m. |
| PD | Predicate disambiguation | batch_69fceaf1e23881908ca24160a638e329 |
completed | May 7, 2026, 7:41 p.m. |
Created at: April 22, 2026, 8:13 a.m.