Triple
T28794888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saigon–Loc Ninh railway line |
E727057
|
entity |
| Predicate | hasFormerNameOfCityAtTerminus |
P40182
|
FINISHED |
| Object | Saigon |
—
|
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: Saigon | Statement: [Saigon–Loc Ninh railway line, hasFormerNameOfCityAtTerminus, Saigon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFormerNameOfCityAtTerminus Context triple: [Saigon–Loc Ninh railway line, hasFormerNameOfCityAtTerminus, Saigon]
-
A.
hasAssociatedCityFormerName
chosen
Indicates that an entity is linked to a city by referencing one of that city's former or historical names.
-
B.
hasFormerNearbyAirportName
Indicates that an entity previously had a nearby airport known by a different name than its current nearby airport name.
-
C.
hasFormerStreetName
Indicates that an entity (such as a street or place) was previously known by a different street name.
-
D.
refersToFormerNameOfComparisonCity
Indicates that the subject entity refers to a previous or former name of the comparison city.
-
E.
formerNameOfCityServed
Indicates that one name was previously used for a city that is (or was) served by a particular entity, before being replaced by its current name.
- 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_69f0319b7c44819085736bcc256185e6 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f78fd5a6388190bfda4bbb2e222e5b |
completed | May 3, 2026, 6:11 p.m. |
| PD | Predicate disambiguation | batch_69f78e2ac3fc819081a45c6841375c8d |
completed | May 3, 2026, 6:04 p.m. |
Created at: April 28, 2026, 6:24 a.m.