Triple
T16902482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shankill Road |
E424472
|
entity |
| Predicate | hasVictimsCount |
P125138
|
FINISHED |
| Object | 10 deaths in 1993 bombing |
—
|
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: 10 deaths in 1993 bombing | Statement: [Shankill Road, hasVictimsCount, 10 deaths in 1993 bombing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVictimsCount Context triple: [Shankill Road, hasVictimsCount, 10 deaths in 1993 bombing]
-
A.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or entity.
-
B.
hasVictims
Indicates that an entity has one or more individuals who have been harmed, injured, or adversely affected by it.
-
C.
numberOfVictimsClaimed
Indicates the reported count of victims associated with a particular event, incident, or action.
-
D.
numberOfVictimsConfirmed
Indicates the confirmed count of victims associated with an event, incident, or situation.
-
E.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
- 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_69d889da3e8c8190a2b118f383f0beac |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e3c8dd2bf08190b1dc099e8a23cd04 |
completed | April 18, 2026, 6:09 p.m. |
| PD | Predicate disambiguation | batch_69e32b9489408190bcb2ede567ff5bf9 |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e34fb7c8c8819086975b7955b7d8ef |
completed | April 18, 2026, 9:32 a.m. |
Created at: April 10, 2026, 5:30 a.m.