Triple
T27017750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nippori area |
E680580
|
entity |
| Predicate | hasNearbyLine |
P187366
|
FINISHED |
| Object | Tokyo Metro Chiyoda Line |
—
|
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: Tokyo Metro Chiyoda Line | Statement: [Nippori area, hasNearbyLine, Tokyo Metro Chiyoda Line]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyLine Context triple: [Nippori area, hasNearbyLine, Tokyo Metro Chiyoda Line]
-
A.
hasNearbyStateLine
Indicates that one location is situated close to the boundary line of a neighboring state.
-
B.
hasAdjacentStationOnLine 11
Indicates that one station is directly next to another station along Line 11, with no other station between them on that line.
-
C.
hasAdjacentStationOnLine1
Indicates that one station is directly next to another station along Line 1 in the network.
-
D.
hasAdjacentStationOnLine 2
Indicates that one station is directly next to another station along Line 2 in the network.
-
E.
hasAdjacentStationOnLine 5
Indicates that one station is directly next to another station along line 5, with no other stations in between on that line.
- 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_69eeeb5450988190bfc9a3c012ac463a |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
| PDg | Predicate description generation | batch_69fb563a28d88190b28345c465c545f8 |
completed | May 6, 2026, 2:54 p.m. |
Created at: April 27, 2026, 7:07 a.m.