Triple
T8439070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 7 (Tehran Metro) |
E199303
|
entity |
| Predicate | hasStations |
P83367
|
FINISHED |
| Object | multiple metro stations in Tehran |
—
|
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: multiple metro stations in Tehran | Statement: [Line 7 (Tehran Metro), hasStations, multiple metro stations in Tehran]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStations Context triple: [Line 7 (Tehran Metro), hasStations, multiple metro stations in Tehran]
-
A.
hasFocalStations
Indicates that an entity is associated with one or more primary or central stations that serve as its main points of focus or operation.
-
B.
hasDedicatedStations
Indicates that specific stations are exclusively assigned or reserved for a particular entity or purpose.
-
C.
hasGhostStations
Indicates that a transportation system or network includes disused, closed, or never-opened stations that still physically exist.
-
D.
hasNotableStation
Indicates that an entity possesses or is associated with a station that is considered notable or significant in some context.
-
E.
hasStationNear
Indicates that one entity has a station located in close proximity to another entity.
- F. None of above. chosen
Provenance (4 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_69ca8314cd6c8190a6b8c2a1096e18f3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe30fba4081908bfdef3faf5baceb |
completed | March 31, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
| PDg | Predicate description generation | batch_69cbe30c2d088190b4cb89adb4e88273 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:08 p.m.