Triple
T5751588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harvey Dent |
E126865
|
entity |
| Predicate | facialDisfigurement |
P8035
|
FINISHED |
| Object | half of face scarred |
—
|
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: half of face scarred | Statement: [Harvey Dent, facialDisfigurement, half of face scarred]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: facialDisfigurement Context triple: [Harvey Dent, facialDisfigurement, half of face scarred]
-
A.
faceliftFeature
Indicates that one entity is a specific feature, component, or aspect involved in a facelift procedure or facelift-related modification of the other entity.
-
B.
facelift
Indicates that an entity undergoes a cosmetic surgical procedure intended to tighten or rejuvenate its facial appearance.
-
C.
defacedWith
Indicates that one entity has been damaged, marred, or vandalized using another entity as the means or material of defacement.
-
D.
faceliftOf
Indicates that one entity is a redesigned, updated, or cosmetically improved version of another existing entity.
-
E.
skinCharacteristic
chosen
Indicates a relationship where an entity is associated with a particular quality, feature, or condition of its skin.
- 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_69c00832aedc81909899801b141fa3b4 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02b52663c8190ab44258468d4296d |
completed | March 22, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69c021ca61688190875bd6107161c284 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:48 p.m.