Triple
T15583843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temple of Bel |
E374566
|
entity |
| Predicate | damageAssessedBy |
P119305
|
FINISHED |
| Object | UNESCO experts after 2015 |
—
|
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: UNESCO experts after 2015 | Statement: [Temple of Bel, damageAssessedBy, UNESCO experts after 2015]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageAssessedBy Context triple: [Temple of Bel, damageAssessedBy, UNESCO experts after 2015]
-
A.
damageAssociatedWith
Indicates a relationship where one entity is linked to causing, contributing to, or being responsible for damage affecting another entity.
-
B.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
C.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
D.
damageBasis
Indicates the underlying reason, cause, or basis on which damage is determined or assessed in a given context.
-
E.
damageAdjusted
Indicates that the amount of damage has been modified from its original value, typically to account for mitigating or amplifying factors.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e47971481909e986dd999354628 |
completed | April 16, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69deda817e9881909b0c66fc9056f7d5 |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f05f708190850f1d8782e132b0 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:11 a.m.