Triple
T4542582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | northern elephant seal |
E107569
|
entity |
| Predicate | femaleMass |
P57596
|
FINISHED |
| Object | up to about 600 kg |
—
|
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: up to about 600 kg | Statement: [northern elephant seal, femaleMass, up to about 600 kg]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleMass Context triple: [northern elephant seal, femaleMass, up to about 600 kg]
-
A.
womenStatus
Indicates the social, legal, economic, or cultural position or condition assigned to women within a given context or system.
-
B.
hasFemaleSpeaker
Indicates that the associated content, event, or communication is spoken or narrated by a female individual.
-
C.
femaleCommonName
Indicates that the associated name is commonly used as a given name for females.
-
D.
featuredGender
Indicates that a particular gender is highlighted, emphasized, or given primary focus in a given context or presentation.
-
E.
hasFemaleEquivalent
Indicates that one entity serves as the female counterpart or equivalent of another entity.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57d3be988190bf118c4a87415613 |
completed | March 20, 2026, 2:21 p.m. |
| PD | Predicate disambiguation | batch_69bd5220e40481908ca2d7e2c43d8531 |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56f6e75481909c487a94a2c2d0ba |
completed | March 20, 2026, 2:17 p.m. |
Created at: March 20, 2026, 1:04 p.m.