Triple
T18605149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macchina di Santa Rosa procession |
E454723
|
entity |
| Predicate | numberOfPorters |
P132760
|
FINISHED |
| Object | about 100 |
—
|
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 100 | Statement: [Macchina di Santa Rosa procession, numberOfPorters, about 100]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPorters Context triple: [Macchina di Santa Rosa procession, numberOfPorters, about 100]
-
A.
portCount
Indicates the number of ports associated with an entity or component.
-
B.
numberOfPortals
Indicates the quantity of portals associated with or contained by a given entity.
-
C.
numberOfPiers
Indicates the quantity of piers associated with or present in a given structure, location, or context.
-
D.
numberOfBerths
Indicates the quantity of berths (sleeping places or docking spaces) associated with an entity.
-
E.
hasPorts
Indicates that an entity is equipped with or provides access points (ports) for connection, communication, or interface with other entities or systems.
- 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_69d8d38bbe7c8190bdec3138e7d413c9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e547535b8c8190ab5a8a92f15f2bcb |
completed | April 19, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69e478cf5e888190a0b1074b0c6525df |
completed | April 19, 2026, 6:40 a.m. |
| PDg | Predicate description generation | batch_69e484121cd48190bf583b4c94636a30 |
completed | April 19, 2026, 7:28 a.m. |
Created at: April 10, 2026, 11:45 a.m.