Triple
T30028667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acurenam |
E762951
|
entity |
| Predicate | hasLargestCityOfCountry |
P163
|
FINISHED |
| Object | Bata |
—
|
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: Bata | Statement: [Acurenam, hasLargestCityOfCountry, Bata]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLargestCityOfCountry Context triple: [Acurenam, hasLargestCityOfCountry, Bata]
-
A.
hasCountryLargestCity
Indicates that a country has, as its largest city, the specified city.
-
B.
isLargestCityIn
chosen
Indicates that one city has the greatest population or size compared to all other cities within a specified region or administrative area.
-
C.
denotesCountryWithLargestCity
Indicates that the subject is a country whose largest city (by population or area) is the specified object.
-
D.
largestCity
Indicates that one city is the most populous or significant urban center within a specified region or entity.
-
E.
areLargestCitiesOf
Indicates that the subject entities are the largest cities within the regions or countries specified by the object entities.
- 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_69f2246ee6e48190b69e837b913b398a |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fd7fdafbe881908a31fcb407af2c34 |
completed | May 8, 2026, 6:16 a.m. |
| PD | Predicate disambiguation | batch_69fd7ef0ea908190b5d83f71565bdb1c |
completed | May 8, 2026, 6:13 a.m. |
Created at: April 29, 2026, 6:49 p.m.