Triple
T641364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Thomas Synagogue |
E16745
|
entity |
| Predicate | earlierSynagogueDestroyedBy |
P17630
|
FINISHED |
| Object | fire |
—
|
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: fire | Statement: [St. Thomas Synagogue, earlierSynagogueDestroyedBy, fire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: earlierSynagogueDestroyedBy Context triple: [St. Thomas Synagogue, earlierSynagogueDestroyedBy, fire]
-
A.
numberOfJewishBusinessesDamaged
Indicates the quantity of Jewish-owned businesses that were harmed or damaged in a given event or context.
-
B.
numberOfJewsKilled
Indicates the quantity of Jewish people who were killed in a given event, context, or time period.
-
C.
hasSynagogueUnion
Indicates that there exists an organizational or formal union connecting one synagogue with another synagogue or group of synagogues.
-
D.
mainCityDestroyed
Indicates that the primary or central city associated with an entity has been destroyed.
-
E.
cityBombed
Indicates that a particular city was subjected to a bombing attack.
- 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_69a4936be1c88190af56540324b57da7 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49f02bc2c8190b8a92b2505768c19 |
completed | March 1, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69a49d0830008190a26ee158ed4dd1fe |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49df0de3c81909721eb391ec94031 |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:36 p.m.