Triple
T26508855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valparai |
E669623
|
entity |
| Predicate | numberOfHairpinBendsOnMainRoad |
P52595
|
FINISHED |
| Object | about 40 |
—
|
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: about 40 | Statement: [Valparai, numberOfHairpinBendsOnMainRoad, about 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfHairpinBendsOnMainRoad Context triple: [Valparai, numberOfHairpinBendsOnMainRoad, about 40]
-
A.
hasNumberOfSlipRoads
Indicates the number of slip roads (on/off ramps) associated with a particular road segment or junction.
-
B.
hasHairpinBends
chosen
Indicates that a route, road, or path includes very sharp, U-shaped turns resembling hairpins.
-
C.
hasNumberOfOneLaneBridges
Indicates the quantity of one-lane bridges associated with or present within a given entity or context.
-
D.
laneCount
Indicates the number of parallel lanes associated with a given road or roadway segment.
-
E.
numberOfConvergingStreets
Indicates the count of distinct streets that meet or intersect at a particular junction or location.
- 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_69eeb319ec70819090834c2591cf5f1e |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69fde49a084081909d99b1e0258169d5 |
completed | May 8, 2026, 1:26 p.m. |
| PD | Predicate disambiguation | batch_69fde1d04bd881909a46ecbbf18dfe59 |
completed | May 8, 2026, 1:14 p.m. |
Created at: April 27, 2026, 1:18 a.m.