Triple
T9596113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Tomatina |
E231535
|
entity |
| Predicate | approximateTomatoQuantity |
P7301
|
FINISHED |
| Object | tens of thousands of kilograms of tomatoes |
—
|
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: tens of thousands of kilograms of tomatoes | Statement: [La Tomatina, approximateTomatoQuantity, tens of thousands of kilograms of tomatoes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateTomatoQuantity Context triple: [La Tomatina, approximateTomatoQuantity, tens of thousands of kilograms of tomatoes]
-
A.
approximateMass
chosen
Indicates that one entity has a mass value that is an estimate or close approximation of the mass of another entity.
-
B.
approximateWeightInPounds
Indicates the estimated weight of an entity expressed in pounds, rather than an exact measured value.
-
C.
approximateNumberOfTulips
Indicates that the relationship specifies an estimated or approximate count of tulips associated with an entity.
-
D.
numberOfJars
Indicates the quantity of jars associated with a given entity or context.
-
E.
estimatedTeaWeight
Indicates the quantified amount of tea that is approximated or predicted in weight rather than precisely measured.
- 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_69ca8482884481908eccdfdf64d6fbf7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9a164c20819093fa863f8f5f79c0 |
completed | April 1, 2026, 10:20 p.m. |
| PD | Predicate disambiguation | batch_69ccd5a359788190b24f82399489f7fe |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:07 p.m.