Triple
T18419135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samir Bannout |
E441973
|
entity |
| Predicate | hasNotablePhysiqueTrait |
P31173
|
FINISHED |
| Object | detailed lower back |
—
|
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: detailed lower back | Statement: [Samir Bannout, hasNotablePhysiqueTrait, detailed lower back]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotablePhysiqueTrait Context triple: [Samir Bannout, hasNotablePhysiqueTrait, detailed lower back]
-
A.
hasPhysicalFeature
chosen
Indicates that one entity possesses or exhibits a specific physical characteristic or feature of another entity.
-
B.
hasNotableStrengthIn
Indicates that an entity possesses a particularly high level of ability, effectiveness, or advantage in a specific area or domain.
-
C.
hasHumanCharacteristic
Indicates that an entity possesses a trait, quality, or behavior typically associated with humans.
-
D.
legCharacteristic
Indicates a characteristic, property, or attribute that specifically pertains to the legs of an entity.
-
E.
hasGivingBodyType
Indicates that one entity serves as the physical or organizational source that gives or grants something to another entity.
- 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_69d8b9eb8a508190a942fd75ebd8b1dc |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e51a2a0fb08190b409ed200a9d86a6 |
completed | April 19, 2026, 6:08 p.m. |
| PD | Predicate disambiguation | batch_69e469bf7f74819096a01173493412c2 |
completed | April 19, 2026, 5:35 a.m. |
Created at: April 10, 2026, 10:47 a.m.