Triple
T4992650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francisco Marto |
E112167
|
entity |
| Predicate | numberOfApparitionsWitnessed |
P17874
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Francisco Marto, numberOfApparitionsWitnessed, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfApparitionsWitnessed Context triple: [Francisco Marto, numberOfApparitionsWitnessed, 6]
-
A.
numberOfSees
Indicates the count of times a seeing or viewing event occurs between entities.
-
B.
numberOfWitnessesHeard
Indicates the count of witnesses whose testimony or statements were heard in a given event or proceeding.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
numberOfInstances
chosen
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
E.
numberOfExecutions
Indicates the count of times a particular action, process, or event has been carried out.
- 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_69bd441be7bc8190b530362d427b97d2 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74249a8c8190952680aee06a9286 |
completed | March 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69bd71492dec8190af4c27a3043b35cc |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:34 p.m.