Triple
T37664471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elder Guardian |
E937789
|
entity |
| Predicate | difficultyAffectsHealth |
P100819
|
FINISHED |
| Object | No |
—
|
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: No | Statement: [Elder Guardian, difficultyAffectsHealth, No]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: difficultyAffectsHealth Context triple: [Elder Guardian, difficultyAffectsHealth, No]
-
A.
hasDifficultyEffect
chosen
Indicates that one entity causes a change in the difficulty level or challenge associated with another entity or activity.
-
B.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
C.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
D.
damageRating
Indicates the assessed level or severity of damage associated with an entity or event.
-
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.
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_69f76ed6df7c8190b018e5baea716ceb |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:18 p.m.