Triple
T17599573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Tikitapu |
E428657
|
entity |
| Predicate | hasApproximateSurfaceArea |
P128175
|
FINISHED |
| Object | 1.5 km² |
—
|
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: 1.5 km² | Statement: [Lake Tikitapu, hasApproximateSurfaceArea, 1.5 km²]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateSurfaceArea Context triple: [Lake Tikitapu, hasApproximateSurfaceArea, 1.5 km²]
-
A.
areaApprox
Indicates that one entity’s area is approximately equal to the area of another entity.
-
B.
surfaceAreaRelative
Indicates the ratio or comparative measure of one entity’s surface area relative to another reference surface area.
-
C.
hasApproximateShape
Indicates that one entity has a shape that is similar to, but not exactly the same as, the shape of another entity.
-
D.
facesArea
Indicates that one entity is oriented toward, overlooks, or has its primary exposure directed toward a specified area.
-
E.
hasBaseArea
Indicates that one entity has a base whose surface area is quantified or associated with 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c4812d48190bf8e899fa8f7fbe4 |
completed | April 19, 2026, 5:46 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fff0348190b899a32da537eaca |
completed | April 18, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69e3bbb50b448190a59dd4be33c76db7 |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:51 a.m.