Triple
T9761208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tour First |
E236673
|
entity |
| Predicate | rankingInFranceByHeight |
P90865
|
FINISHED |
| Object | one of the tallest office towers in France |
—
|
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: one of the tallest office towers in France | Statement: [Tour First, rankingInFranceByHeight, one of the tallest office towers in France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingInFranceByHeight Context triple: [Tour First, rankingInFranceByHeight, one of the tallest office towers in France]
-
A.
rankInCityByHeight
Indicates the relative ordering of entities within a specific city based on their height, such as which is tallest, second tallest, and so on.
-
B.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
C.
economicRankInFrance
Indicates the relative economic standing or ranking of an entity within the context of France’s economy.
-
D.
regionRankByHeight
Indicates the relative ordering of regions based on their height or elevation.
-
E.
rankByHeightWorld
Indicates an ordering of entities based on their relative height compared to all others in the world.
- 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_69ca84d64f6c8190a4ed4e9f5936eda5 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda04ad9008190badfcebe2072ab83 |
completed | April 1, 2026, 10:46 p.m. |
| PD | Predicate disambiguation | batch_69cd03d0772c8190bd1750cf1cfba309 |
completed | April 1, 2026, 11:38 a.m. |
| PDg | Predicate description generation | batch_69cd081a9c5c819093439be7e802ff85 |
completed | April 1, 2026, 11:57 a.m. |
Created at: March 30, 2026, 8:25 p.m.