Triple
T14637968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lys |
E343653
|
entity |
| Predicate | populationTrait |
P103508
|
FINISHED |
| Object | fair hair |
—
|
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: fair hair | Statement: [Lys, populationTrait, fair hair]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationTrait Context triple: [Lys, populationTrait, fair hair]
-
A.
demographicsCharacteristic
Indicates that one entity serves as a demographic attribute or characteristic (such as age, gender, ethnicity, etc.) that describes or classifies another entity.
-
B.
populationPattern
Indicates how a population is distributed, structured, or changes over space or time in relation to other entities or conditions.
-
C.
breedingTrait
chosen
Indicates that an entity possesses a heritable characteristic that is relevant or targeted in a breeding process or program.
-
D.
demographicCharacteristic
Indicates that one entity specifies or describes a demographic attribute or feature (such as age, gender, ethnicity, or similar population-related trait) of another entity.
-
E.
studiesTrait
Indicates that one entity conducts research or examination focused on a particular trait or characteristic of 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4aca6448190adf1042dfbfef716 |
completed | April 14, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69de657359c88190b082e3e9f86fc1d7 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:26 a.m.