Triple
T13349939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1967 Shag Harbour incident |
E318043
|
entity |
| Predicate | witnessCount |
P109137
|
FINISHED |
| Object | multiple witnesses |
—
|
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: multiple witnesses | Statement: [1967 Shag Harbour incident, witnessCount, multiple witnesses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: witnessCount Context triple: [1967 Shag Harbour incident, witnessCount, multiple witnesses]
-
A.
numberOfWitnessesHeard
Indicates the count of witnesses whose testimony or statements were heard in a given event or proceeding.
-
B.
witnesses
Indicates that one entity observes an event, action, or situation involving another entity, typically as a bystander or observer.
-
C.
notableWitness
Indicates that one entity is a significant or prominent witness to an event, action, or situation involving another entity.
-
D.
socialWitness
Indicates that one entity observes, attends, or is present for another entity’s social interaction or socially significant event.
-
E.
hasWitnessType
Indicates that an event, incident, or situation is associated with a specific category or type of witness involved.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e8c2f1c819094f0970f35f18afa |
completed | April 11, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69d98f6e53d88190bd6aa42f69b10ffb |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99073e4708190843bda3a1ae78f43 |
completed | April 11, 2026, 12:06 a.m. |
Created at: April 9, 2026, 9:31 p.m.