Triple
T1671548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai Tower |
E36135
|
entity |
| Predicate | rankInShanghai |
P27607
|
FINISHED |
| Object | tallest building in Shanghai |
—
|
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: tallest building in Shanghai | Statement: [Shanghai Tower, rankInShanghai, tallest building in Shanghai]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInShanghai Context triple: [Shanghai Tower, rankInShanghai, tallest building in Shanghai]
-
A.
rankInShanghaiByHeightCurrent
Indicates the position an entity currently holds in a ranking of heights within Shanghai, ordered from tallest to shortest.
-
B.
rankInShanghaiByHeightAtCompletion
Indicates the numerical position an entity holds in a Shanghai-wide ranking ordered by its height at the time it was completed.
-
C.
rankInCityByHeight
chosen
Indicates the relative ordering of entities within a specific city based on their height, such as which is tallest, second tallest, and so on.
-
D.
areaRank
Indicates the relative ordering or position of an entity based on the size of its area compared to others.
-
E.
rankingByLengthInChina
Indicates that entities are ordered or evaluated based on their length within the context of China.
- 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab272a653481908f48aa1eed5de8a4 |
completed | March 6, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69aa61b2f6288190b2348ef7d7e4672d |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:29 p.m.